阅读说明：本技术 跨模态数据的匹配方法、装置、设备及介质 (Cross-modal data matching method, device, equipment and medium ) 是由 蒋永余 王俊艳 王璋盛 曹家 罗引 王磊 于 2021-10-14 设计创作，主要内容包括：本公开涉及一种跨模态数据的匹配方法、装置、设备及介质。其中,跨模态数据的匹配方法包括：获取待匹配数据和候选数据,待匹配数据和候选数据的数据模态不同；对待匹配数据和候选数据进行量子化表示,得到待匹配数据与候选数据在量子复合系统内的分布信息；基于分布信息,提取待匹配数据与候选数据之间的量子干涉特征数据；在量子干涉特征数据满足预设匹配条件的情况下,确定候选数据和待匹配数据相匹配。根据本公开实施例,能够提高跨模态信息的匹配精度。(The disclosure relates to a cross-modal data matching method, device, equipment and medium. The cross-modal data matching method comprises the following steps: acquiring data to be matched and candidate data, wherein the data to be matched and the candidate data have different data modalities; carrying out quantization expression on the data to be matched and the candidate data to obtain distribution information of the data to be matched and the candidate data in the quantum composite system; extracting quantum interference characteristic data between the data to be matched and the candidate data based on the distribution information; and under the condition that the quantum interference characteristic data meet the preset matching condition, determining that the candidate data are matched with the data to be matched. According to the embodiment of the disclosure, the matching precision of cross-modal information can be improved.)

1. A cross-modal data matching method is characterized by comprising the following steps:

acquiring data to be matched and candidate data, wherein the data to be matched and the candidate data have different data modalities;

carrying out quantization expression on the data to be matched and the candidate data to obtain distribution information of the data to be matched and the candidate data in a quantum composite system;

extracting quantum interference characteristic data between the data to be matched and the candidate data based on the distribution information;

and under the condition that the quantum interference characteristic data meet a preset matching condition, determining that the candidate data are matched with the data to be matched.

2. The method according to claim 1, wherein the extracting quantum interference feature data between the data to be matched and the candidate data based on the distribution information comprises:

carrying out probability distribution calculation on the distribution information to obtain probability density distribution parameters of the data to be matched and the candidate data in the quantum composite system;

and carrying out feature extraction processing on the probability density distribution parameters to obtain the quantum interference feature data.

3. The method of claim 1, wherein the quantum composite system comprises a first subsystem consisting of data features of the data to be matched and a second subsystem consisting of data features of candidate data.

4. The method according to claim 3, wherein the extracting quantum interference feature data between the data to be matched and the candidate data based on the distribution information specifically comprises:

performing probability distribution calculation on the distribution information to obtain probability density distribution parameters of the data to be matched and the candidate data in the quantum composite system;

performing dimensionality reduction on the probability density distribution parameters of the quantum composite system to obtain the probability density distribution parameters of the candidate data in the dimensionality of the second subsystem;

and performing feature extraction on the probability density distribution parameters of the dimension of the second subsystem to obtain the quantum interference feature data.

5. The method of claim 4,

the extracting the features of the probability density distribution parameters of the second subsystem dimension to obtain the quantum interference feature data specifically comprises:

inputting the probability density distribution parameters of the second subsystem dimension into a pre-trained feature extraction model to obtain the effective probability distribution features of the candidate data;

and processing the effective probability distribution characteristics of the candidate data by using an attention mechanism and the data characteristics of the data to be matched to obtain the quantum interference characteristic data.

6. The method of claim 1, wherein the distribution information is represented in a vector form;

the performing quantization representation on the data to be matched and the candidate data to obtain distribution information of the data to be matched and the candidate data in a quantum composite system includes:

acquiring a plurality of first data features of the data to be matched and a plurality of second data features of the candidate data;

performing feature fusion on the plurality of first data features and the plurality of second data features to obtain a superposition state vector;

and taking the superposition state vector as distribution information in a vector form.

7. The method of claim 6, wherein the feature fusing the first data features and the second data features to obtain a superposition state vector comprises:

combining any first data characteristic and any second data characteristic to obtain a plurality of characteristic groups; for each feature group, carrying out tensor product operation processing on the first data features in each feature group and the second data features in each feature group to obtain a quantum composite state vector;

and accumulating the quantum composite state vectors of the plurality of feature groups to obtain the superposition state vector.

8. The method according to claim 1, wherein the preset matching condition comprises that a matching degree score corresponding to the quantum interference feature data meets a preset score condition;

under the condition that the quantum interference characteristic data meet a preset matching condition, determining that the candidate data is matched with the data to be matched specifically comprises:

inputting the quantum interference characteristic data into a pre-trained matching degree prediction model to obtain a matching degree score of the data to be matched and the candidate data;

and under the condition that the matching degree score meets the preset score condition, the data to be matched and the candidate data are matched with each other.

9. The method of claim 8,

the candidate data belongs to a candidate data set, and the preset score condition comprises:

the sorting position of the matching degree score corresponding to the quantum interference characteristic data in the matching degree score corresponding to the candidate to-be-detected data set is less than or equal to a preset number; alternatively, the first and second electrodes may be,

and the matching degree score corresponding to the matching degree characteristic parameter is greater than a preset score threshold value.

10. The method of claim 1,

the data to be matched and the candidate data are any one of text data, image data, video data and audio data respectively.

11. An apparatus for matching cross-modal data, comprising:

the data acquisition unit is configured to acquire data to be matched and candidate data, and the data to be matched and the candidate data have different data modalities;

the quantization representation unit is configured to perform quantization representation on the data to be matched and the candidate data to obtain distribution information of the data to be matched and the candidate data in a quantum composite system;

the feature extraction unit is configured to extract quantum interference feature data between the data to be matched and the candidate data based on the distribution information;

and the data matching unit is configured to determine that the candidate data is matched with the data to be matched under the condition that the quantum interference characteristic data meets a preset matching condition.

12. A device for matching cross-modal data, comprising:

a processor;

a memory for storing executable instructions;

wherein the processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the matching method across modality data according to any one of claims 1 to 10.

13. A computer-readable storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, causes the processor to implement a matching method across modal data as recited in any of claims 1-10 above.

Technical Field

The present disclosure relates to the field of information matching technologies, and in particular, to a method, an apparatus, a device, and a medium for matching cross-modal data.

Background

In the development process of the information matching technology, the problem of how to perform cross-modal information matching gradually draws attention of related personnel.

At the present stage, a neural network model is often adopted to calculate the similarity of the cross-modal information, and then the cross-modal information matching is performed according to the similarity. However, due to the problems of complex modes of cross-modal information matching, large cognitive difference between different modal information, and the like, the calculation accuracy of the scheme in the cross-modal information matching technology is often low.

Therefore, a technical solution capable of improving the matching accuracy of cross-modal information is needed.

Disclosure of Invention

To solve the above technical problem or at least partially solve the above technical problem, the present disclosure provides a method, an apparatus, a device, and a medium for matching cross-modal data.

In a first aspect, the present disclosure provides a cross-modal data matching method, including:

acquiring data to be matched and candidate data, wherein the data to be matched and the candidate data have different data modalities;

carrying out quantization expression on the data to be matched and the candidate data to obtain distribution information of the data to be matched and the candidate data in the quantum composite system;

extracting quantum interference characteristic data between the data to be matched and the candidate data based on the distribution information;

and under the condition that the quantum interference characteristic data meet the preset matching condition, determining that the candidate data are matched with the data to be matched.

In a second aspect, the present disclosure provides a cross-modal data matching apparatus, including:

the data acquisition unit is configured to acquire data to be matched and candidate data, and the data modalities of the data to be matched and the candidate data are different;

the quantization expression unit is configured to perform quantization expression on the data to be matched and the candidate data to obtain distribution information of the data to be matched and the candidate data in the quantum composite system;

the characteristic extraction unit is configured to extract quantum interference characteristic data between the data to be matched and the candidate data based on the distribution information;

and the data matching unit is configured to determine that the candidate data is matched with the data to be matched under the condition that the quantum interference characteristic data meets the preset matching condition.

In a third aspect, the present disclosure provides a cross-modal data matching device, including:

a processor;

a memory for storing executable instructions;

the processor is used for reading the executable instructions from the memory and executing the executable instructions to realize the matching method of the cross-modal data of the first aspect.

In a fourth aspect, the present disclosure provides a computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to implement the method of matching across modal data of the first aspect.

Compared with the prior art, the technical scheme provided by the embodiment of the disclosure has the following advantages:

the cross-modal data matching method, device, equipment and medium of the embodiments of the present disclosure can obtain distribution information of the data to be matched and the candidate data in the quantum composite system by performing quantization representation on the data to be matched and the cross-modal data, and extract and obtain quantum interference characteristic data between the candidate data and the data to be matched from the distribution information. The quantum interference characteristic data can reflect the cognition of a user on the information commonly expressed by the cross-modal data, so that the candidate data and the data to be matched can be matched from a cognitive level by using the quantum interference characteristic, and the matching precision of the cross-modal information is improved.

Drawings

The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and features are not necessarily drawn to scale.

FIG. 1 shows a schematic diagram of a double slit interference experiment of light;

fig. 2 is a schematic flowchart illustrating a cross-modal data matching method according to an embodiment of the present disclosure;

FIG. 3 is a flow chart illustrating another cross-modality data matching method provided by an embodiment of the present disclosure;

fig. 4 is a schematic flowchart illustrating a further cross-modal data matching method provided by an embodiment of the present disclosure;

fig. 5 is a schematic flowchart illustrating a further cross-modal data matching method according to an embodiment of the present disclosure;

fig. 6 is a schematic flowchart illustrating a further cross-modal data matching method according to an embodiment of the present disclosure;

FIG. 7 illustrates a logical diagram of an exemplary cross-modality data matching method provided by an embodiment of the present disclosure;

FIG. 8 illustrates a flowchart of an exemplary cross-modality data matching method provided by an embodiment of the present disclosure;

fig. 9 is a schematic diagram illustrating a news text to be published according to an embodiment of the present disclosure;

fig. 10 is a schematic diagram illustrating the image-text matching between the news text to be published and the news image in the database according to the embodiment of the disclosure;

FIG. 11 is a schematic diagram illustrating a publishable news information provided by an embodiment of the present disclosure;

fig. 12 is a schematic structural diagram illustrating a cross-modal data matching apparatus according to an embodiment of the present disclosure;

fig. 13 shows a schematic structural diagram of a cross-modal data matching device according to an embodiment of the present disclosure.

Detailed Description

Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.

It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order, and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.

The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.

It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.

It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.

The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.

In the development process of the information dissemination field, as multi-modal data can more accurately and intuitively convey information compared with single-modal data, cross-modal information is rapidly developed. For example, with the continuous development of the media industry, the number of various online media platforms increases day by day, and the number of electronic news also increases exponentially, and becomes one of the main sources for obtaining information in the daily life of users. Unlike previous media platforms that delivered information only in text form, media platforms are increasingly inclined to co-express their news information using multi-modal form data (e.g., text-image pairs, text-video peers).

In the field of cross-modal information, the problem of how to match cross-modal information gradually draws attention of related personnel at each line and each boundary. For example, how to select the most suitable news image for news text is one of the research directions in the media industry.

Taking cross-modal information matching between a text and an image as an example, in the past, a manual search mode is usually adopted to perform screening in a large number of images according to text contents, which consumes a great amount of time and energy of human beings. In recent years, with the progress of artificial intelligence technology, how to help relevant people reduce the workload of image screening and improve efficiency by using a computer is an urgent need, and the attention of many researchers is attracted. Therefore, researchers have proposed a teletext matching technique for recommending appropriate images based on textual content, and there are two basic problems that generally need to be solved by this task: how to characterize text and images; how to combine the features of text and images and accurately measure the relevance of the two.

The current image-text matching method for learning the correlation between images and texts can be roughly divided into two types: global correlation and local correlation. The global correlation mainly learns the correlation between the whole image and the sentence, namely, the whole image and the sentence are mapped to a universal semantic space to calculate the image-text correlation.

In a related technology, a deep Convolutional Neural Network (CNN) may be used to encode an image and a Recurrent Neural Network (RNN) may be used to encode a sentence, and then a Hinge-based triple learning Loss function (a triple metric learning Loss function based on Hinge theory) is used as a distance metric to measure the matching degree between the image and the text.

In another related art, hard samples (hard neighbors) are used in the triple Loss (triple Loss) function and the matching effect is significantly improved.

In yet another related technique, a generation process may be incorporated into cross-modal feature embedding to learn global abstract features and local hierarchy features. Local correlation mainly learns the correlation between image local regions and words, i.e. the potential visual language correlation is considered at the level of image local regions and words.

In still another related technique, image regions may be detected and encoded using fast R-CNN (i.e., a target detection network) based on SCAN model (i.e., an image-text matching model), features thereof may be extracted, features of each word in a sentence may be extracted using Bi-GRU (i.e., a recurrent neural network) model, and finally, a degree of matching between an image and a text may be obtained through Stack Crossing Attention mechanism (Stack Crossing attachment).

However, the applicant has studied and found that a cross-modal matching task, such as a teletext matching task, is not only a matching process between data, but also a complex and subjective multi-modal cognitive process.

The applicant finds that the correlation of cross-modal data calculated by the existing image-text matching technology is different from the real experimental result through research. Through research, the difference is mainly caused by the following two reasons: on one hand, the information which is jointly expressed by different modes entangled influences the real expression intention of the information expression person; on the other hand, information commonly expressed by different modalities entangled together can affect the final information understanding process of an information reader. Therefore, the user's knowledge of the multimodal information co-expression affects the accuracy of the matching across the modal data.

The applicant refers to this phenomenon as cognitive interference phenomenon, that is, in the cognitive state of the user, the relevance judgment of the user is not the superposition of simple matching evidences (e.g. co-occurrence evidences) and cannot be explained by the classical probability theory. However, the global correlation and local correlation graph-text matching models used in the related technologies mainly surround the multi-modal feature extraction method and how to train an excellent correlation calculation network to improve the correlation calculation accuracy of cross-modal data, and the cognitive interference effect between cross-modal information is not considered, so that the proposed model does not consider the cross-modal data matching task from the cognitive aspect, which often causes errors in cross-modal data correlation judgment and further causes errors in cross-modal data matching accuracy.

Based on the above, the applicant provides a cross-modal data matching scheme, which can be applied to a cross-modal data matching scenario. For example, it can be applied to a specific scene matching a suitable news image to news text. According to the cross-modal data matching scheme provided by the embodiment of the disclosure, the quantum interference characteristic data can reflect the cognition of the user on the information commonly expressed by the cross-modal data, so that the quantum interference characteristic can be used for matching the candidate data and the data to be matched from the cognitive level, and the matching accuracy of the cross-modal information is improved.

Before beginning to introduce the matching scheme across modal data provided by the embodiments of the present disclosure, for ease of understanding, the embodiments of the present disclosure will first describe related art.

First, quantum interference effect.

It is derived from the well-known "double slit interference experiment of light" in physical history. Fig. 1 shows a schematic diagram of a double slit interference experiment of light. As shown in FIG. 1, the double-slit interference experiment is simple, a candle 101 is found and lighted, and a first paper 102 is placed behind the candle 101, wherein a small hole is formed in the first paper 102, so that a point light source is formed after light emitted by the candle 101 penetrates through the first paper 102. Then, a second sheet 103 is placed behind the first sheet 102, except that the second sheet 103 is slit in parallel. In the imagine that the light from the candle 101 passing through these two slits must leave two parallel and corresponding bright lines on the wall 104. However, this is not the case. After the light passes through the two slits, a row of parallel "zebra stripes" of bright lines remains on the wall 104.

Fig. 2 shows a schematic flowchart of a cross-modal data matching method according to an embodiment of the present disclosure.

In this disclosure, an execution subject of each step of the cross-modal data matching method may be a device or a module with a computing function, such as a desktop calculator, a notebook computer, a cloud server, a server cluster, and the like, which is not particularly limited.

As shown in fig. 2, the matching method across modal data may include the following steps.

And S210, acquiring the data to be matched and the candidate data.

In the embodiment of the disclosure, the data modality of the data to be matched and the data candidate are different. That is, the data to be matched and the candidate data may be different types of multimedia data.

In some embodiments, the data to be matched and the candidate data are any one of text data, image data, video data, and audio data, respectively. Illustratively, if the data to be matched is text data, the candidate data is any one of text data, image data, video data, and audio data other than the text data. For example, the candidate data may be image data.

In one example, in a news distribution scenario, the data to be matched may be news text and the candidate data may be news pictures.

In some embodiments, in order to match the data to be matched with the candidate data with a high matching degree, a plurality of selectable modality data in the candidate data set may be respectively used as the candidate data, and the data to be matched and the candidate data are matched according to the cross-modality matching method of the embodiment of the present disclosure. And the modality of the data in the candidate data set is different from that of the data to be matched. Alternatively, the candidate data set may be a collection of data in a candidate database. For example, in a news release scenario, the candidate database may be a photo library of a media platform or a photo library of a web page, which is not limited thereto.

And S220, performing quantization expression on the data to be matched and the candidate data to obtain distribution information of the data to be matched and the candidate data in the quantum composite system. The quantum composite system can be a quantum system formed by the data to be matched and the candidate data.

In some embodiments, if the data to be matched can be extracted from the data to be matchedlA first data characteristic ofP ₁ 、P ₂ 、…、P _l (ii) a Extracting from candidate datamA second data characteristic ofI ₁ 、I ₂ 、…、I _m . The quantum composite system may be considered to be defined bylVector sum of first feature datamAnd a quantum system in a vector space formed by the vectors of the second feature data. Wherein the content of the first and second substances,landmis an integer greater than 1.

Specifically, if a vector of any first data feature, any second data feature may constitute a quantum complex state vector within the quantum complex system. The distribution information of the data to be matched and the candidate data in the quantum composite system can be represented by a superposition state vector formed by a plurality of quantum composite state vectors in the quantum composite system.

Accordingly, the superposition state vectorCan be expressed by the following formula (1):

（1）

in the formula (1),is shown asiA first data characteristicP _i The corresponding weight coefficient of the weight is,is shown asjSecond data characteristicI _j The corresponding weight coefficient of the weight is,representing a tensor product operation. Wherein the content of the first and second substances,is represented byiA first data characteristicP _i And a firstjSecond data characteristicI _j And forming quantum composite state vector. Alternatively,、the method can be preset or trained, and the specific setting mode is not limited. Wherein the content of the first and second substances,iis not more thanlAny positive integer of (a) to (b),jis not more thanmAny positive integer of (a).

The applicant has shown through research that the method for carrying out quantization representation on the cross-modal data through the tensor product form can utilize the superposition state vectorThe interaction between the dimensions of the feature vectors characterizing the cross-modal data enables modeling of all possible combinations of high level semantics of the cross-modal data, thus obtaining superimposed state vectorsHas stronger expression capability to common expression information formed by cross-modal data。

In one example, to ensure compliance with the relevant constraints of the quantum system, the weighting coefficients need to satisfy the respective normalization conditions.

In particular, the method comprises the following steps of,lthe weight coefficients corresponding to the first characteristic data satisfy the following formula (2):

（2）

and the number of the first and second groups,mthe weight coefficients corresponding to the second characteristic data satisfy the following formula (3):

（3）

accordingly, S220 specifically includes the following steps a1 to A3.

Step A1, obtaining a plurality of first data features of the data to be matched and a plurality of second data features of the candidate data.

In one embodiment, feature extraction may be performed on the data to be matched to obtainlA first data characteristic. Accordingly, can uselThe first data feature set P is composed of first data features to represent the data to be matched.

The first set of data features P may be as shown in equation (4):

P={P ₁ ,P ₂ ,…,P _l }（4）

in one example, if the data to be matched is text data, the text can be extractedlThe feature of each paragraph may be the feature of each sentence of the text, or the feature of each word of the text, and the feature extraction granularity is not particularly limited. Alternatively, text feature extraction may be performed using a pre-trained transform bi-directional encoding Representation (Bert) model or the like. It should be noted that the method can also be applied to the field of the document such as the term frequency-inverse document frequency (TF-IDF)The method for extracting text features such as Word to vector (Word 2 vector) model and countvector (i.e. a text feature extraction function) performs feature extraction, and the specific extraction method is not limited.

In one particular example, the first of the text data may beiA paragraphp _i After the operations of symbol removal, word segmentation, dictionary mapping and the like, the first model is used for coding to obtain the second modeliFeature vector of individual paragraphP _i 。

Accordingly, the firstiFeature vector of individual paragraphP _i Can be expressed as the following formula (5):

P _i =bert(p _i )（5）

in another example, if the data to be matched is an image, the image may be extractedmFeatures of the individual image regions. Specifically, the image features can be extracted by adopting a pre-trained fast RCNN model. It should be noted that, in the embodiment of the present disclosure, the image Features may also be extracted by using a Scale-Invariant Features Transform (SIFT) model, a Speeded Up Robust Features (SURF) model, a Histogram of Oriented Gradients (HOG) algorithm, a Difference of Gaussian (DOG) algorithm, a Local Binary Pattern (LBP) feature extraction algorithm, and the like, and the specific feature extraction method is not limited.

In another example, if the data to be matched is a video, the features of one or more video frames may be extracted by using the image extraction algorithm, or the video data may be input into a pre-trained video feature extraction model to obtain the features of the video data, and the feature extraction manner is not particularly limited. The video feature extraction model may be a three-dimensional Convolutional Network (3D CNN) model.

In yet another example, if the data to be matched is audio, voice recognition (Automatic) may be utilizedASR) algorithm converts audio data into text, and then performs text feature extraction on the text to obtain text featureslA first data characteristic. For the text feature extraction algorithm, reference may be made to the relevant descriptions of the above parts of the embodiments of the present disclosure, which are not described again. Alternatively, the features of the audio may be extracted by using a pre-trained language feature extraction model or a pre-trained speech feature extraction algorithm, which is not particularly limited. The speech feature extraction algorithm may be a Linear Prediction analysis (LPC) algorithm, a Perceptual Linear Prediction coefficient (PLP) algorithm, or other methods capable of extracting speech features, and is not particularly limited.

In another embodiment, the candidate data may be subjected to feature extraction to obtainmA second data characteristic. Accordingly, can usemAnd a second data feature set I formed by the second data features represents the data to be matched.

The second set of data features I may be as shown in equation (6):

I={I ₁ ,I ₂ ,…,I _m }（6）

for specific feature extraction content of the candidate data, reference may be made to the relevant description of the feature extraction manner of the candidate data in the above-mentioned part of the embodiment of the present disclosure, and details are not repeated here.

In one example, if the candidate data is a picture, the target detection frame is framed in the picturejAn image areai _j Inputting a feature vector obtained by a pre-trained fast RCNN modelI _j Can be shown as equation (7):

i _j =Faster_RCNN(I _j )（7）

step a2, combining any first data feature and any second data feature to obtain a plurality of feature groups.

Illustratively, the 1 st first data feature may be respectively associated withmA second characteristic data componentmA set of characteristics of the plurality of characteristics,the 2 nd first data feature may be respectively associated withmA second characteristic data componentmCharacteristic set, similarly, the firstlThe first data characteristic may be respectively associated withmA second characteristic data componentmAnd (4) a feature group.

Step A3, performing feature fusion on the plurality of first data features and the plurality of second data features to obtain a superposition state vector, and taking the superposition state vector as distribution information in a vector form.

In one example, the superposition state vector may be calculated based on equation (1) above. Accordingly, step A3 may include steps a31 through a 33.

Step A31, for each feature group, performing tensor product operation processing on the first data feature in each feature group and the second data feature in each feature group to obtain a quantum composite state vector.

Exemplarily, in the first placeiA first data characteristic and a second data characteristicjSecond data characteristicI _j For example, step A3 may include step a311 and step a 312.

Step A311, for the secondiThe first data characteristic is weighted, i.e. the first data characteristic is calculatediA first data characteristic and a second data characteristiciThe weight coefficient corresponding to the first data characteristicTo obtain the product ofiA first weighting characteristic。

And, step A32, for the second stepjThe second data characteristic is weighted, i.e. the first data characteristic is calculatedjA second data characteristic andjthe weight coefficient corresponding to the second data characteristicTo obtain the product ofjA second weighting characteristic。

And A32, carrying out tensor product operation on each first weighted feature and each second weighted feature to obtain a quantum composite state vector.

Continuing with the previous example, foriA first weighting characteristicAnd a firstjA second weighting characteristicPerforming tensor product operation to obtain the secondiA first data characteristicP _i And a firstjSecond data characteristicI _j Component quantum complex state vector。

Step a33, accumulating the multiple quantum complex state vectors obtained according to the multiple first weighting characteristics and the multiple second weighting characteristics to obtain a superposition state vector as shown in formula (1).

It should be noted that, the superposition state vector may also be obtained in other forms based on a plurality of first feature data and second feature data, and the specific calculation formula and the calculation step of the superposition state vector are not specifically limited in the embodiment of the present application.

In other embodiments, S220 may further specifically include: and inputting the data to be matched and the candidate data into a pre-trained quantization expression model to obtain the superposition state vector. The quantization expression model may be a Neural network model capable of outputting a superposition state vector when the matching mode data and the candidate data are input, such as a CNN model, an RNN model, a Deep Neural Network (DNN), or an improved network of the foregoing Networks, to perform quantization expression.

And S230, extracting quantum interference characteristic data between the data to be matched and the candidate data based on the distribution information.

In some embodiments, a quantum composite system includes a first subsystem consisting of data features of data to be matched and a second subsystem consisting of data features of candidate data.

Accordingly, fig. 3 shows a flowchart of another cross-modal data matching method provided by the embodiment of the present disclosure. Fig. 3 is different from fig. 2 in that S230 may specifically include S231 to S233.

And S231, carrying out probability distribution calculation on the distribution information to obtain probability density distribution parameters of the data to be matched and the candidate data in the quantum composite system.

For example, the probability density distribution parameter of the data to be matched and the candidate data in the quantum composite system can be expressed as。

And S232, performing dimensionality reduction on the probability density distribution parameters of the quantum composite system to obtain the probability density distribution parameters of the candidate data in the dimensionality of the second subsystem.

Illustratively, the operation result of step B1 can be obtained byPerforming bias trace operation to obtain reduced density operatorρ ^I I.e. a probability density distribution parameter representing the candidate data in the second subsystem dimension.

In particular, the reduction of density operatorsρ ^I Can be shown as equation (8):

（8）

wherein the content of the first and second substances,. Coefficient of performance. Wherein the content of the first and second substances,M _s the data matching method is characterized by comprising the step of matching a feature matrix for classical similarity, wherein the feature matrix is used for representing data matching features which are used by existing cross-modal matching models between data to be matched and candidate data, accord with classical probability theory and are on a data matching level.M _IN The method is a quantum interference characteristic matrix which is used for representing data to be matched and candidate data and quantum interference characteristic data on a user cognitive level.

It should be noted that, in the scheme of directly using the superposition state vector to perform subsequent calculation, the requirement on the calculation resource is higher due to the higher dimension of the superposition state vector of the quantum composite system. In the embodiment of the present disclosure, the dimension reduction processing may be performed on the superposition state vector of the quantum composite system through step B2, so as to reduce the requirement of the subsequent steps on the calculation resources, and improve the calculation efficiency.

And S233, performing feature extraction on the probability density distribution parameters of the second subsystem dimension to obtain quantum interference feature data.

In one embodiment, fig. 4 is a flowchart illustrating a further cross-modal data matching method provided in an embodiment of the present disclosure. Fig. 4 is different from fig. 3 in that S233 may specifically include S2331 to S2332.

Step S233 may be embodied as S2331 and S2332.

S2331, inputting the probability density distribution parameters of the second subsystem dimension into a pre-trained feature extraction model to obtain the effective probability distribution features of the candidate data.

In one example, an n-gram Window Convolition Network (n-gram) may be utilized to extract the effective probability distribution features in the candidate data. In particular, n different sizes of convolution kernels may be utilized in combinationρ ^I And extracting the n-gram correlation characteristics to be used as effective probability distribution characteristics in the candidate data.

Optionally, the convolution kernel size corresponding to the convolution layer size h is h x h, and the n convolution kernel sizes h form a set{2,3,4,5}, i.e., the convolution kernel size h ∈ {2,3,4,5}, will pass through the CNN layerρ ^I The specific formula for mapping to the correlation feature of the n-gram is shown in the following formula (9) and formula (10):

（9）

（10）

wherein the content of the first and second substances,representing the output result of the first layer of convolutional layers convolved with a convolutional kernel size h x h, a functionRepresenting a Max-firing operation in the convolutional layer of the first layer (i.e., a downsampling operation in a CNN network), symbolsWhich represents the merging operation, is performed,G ₂ and the operation output result of the second layer convolution layer is shown. It should be noted that, in the embodiment of the present disclosure, other downsampling operations besides the Max-posing operation may be used, or the downsampling operation is not used, which is not limited. It should be noted that, by Max-posing operation, strong features can be extracted from the output result of the first layer convolution layer, and weak features are discarded, thereby improving matching accuracy and calculation efficiency.

In this embodiment, the n-gram Window Container Network is utilized to capture more finely and with multiple granularitiesρ ^I The matching precision is improved due to the medium effective probability distribution characteristics.

It should be noted that other models may also be used to extract the effective probability distribution features in the candidate data, such as a CNN model, an RNN model, a DNN model, or a modified network of the above networks to extract the effective features, and the embodiment of the present disclosure does not specifically limit the type of the model for extracting the effective probability distribution features.

S2332, processing the effective probability distribution characteristics of the candidate data by using a Attention Mechanism (Text Attention Mechanism) and the data characteristics of the data to be matched to obtain quantum interference characteristic data. The quantum interference characteristic data comprises quantum interference characteristics between the data to be matched and the candidate data. As another alternative, the quantum interference feature data may include quantum interference features between the data to be matched and the candidate data, and data matching features at a data matching level, which are commonly used in existing cross-mode matching models.

Alternatively, the quantum interference signature data may be represented as a feature vectorx _att Which satisfies the following formula (11) and formula (12):

（11）

（12）

wherein the measurement operator，Can represent the firstiA first data characteristicP _i A probability density distribution parameter in a second subsystem dimension.

The feature vector calculated by the formula (12)x _att The method not only comprises the data matching characteristics which are commonly used in the existing cross-modal matching model and are on the data matching level, but also comprises the quantum interference characteristics on the user cognition level, and further utilizes the characteristic vectorx _att Can carry out cross-modal data matching from a cognitive level and a data matching level, therebyCross-modal data matching on multiple layers can be realized, and matching precision is improved.

In another embodiment, in addition to extracting quantum interference features in steps S2331 and 2332 described above, the quantum interference features may be extracted from the probability density distribution parameters of the second subsystem dimension using a pre-trained feature extraction model. The feature extraction model may be, but is not limited to, a CNN model, an RNN model, a DNN model, a Long Short-Term Memory (LSTM) model, or an improved model thereof.

In other embodiments, fig. 5 is a schematic flowchart illustrating a further cross-modal data matching method according to an embodiment of the present disclosure. Fig. 5 differs from fig. 2 in that S230 may specifically include S234 and S235.

And S234, carrying out probability distribution calculation on the distribution information to obtain probability density distribution parameters of the data to be matched and the candidate data in the quantum composite system.

S234 is similar to S231, and reference may be made to the related description of S231 in the above section of the disclosure, which is not repeated herein.

And S235, performing feature extraction processing on probability density distribution parameters of the data to be matched and the candidate data in the quantum composite system to obtain quantum interference feature data.

The specific implementation of S235 is similar to that of S232-S233, except that the probability density distribution parameters of the data to be matched and the candidate data in the quantum composite system are obtainedAnd then, the dimension reduction processing is not required, and the feature extraction processing is directly carried out by using the dimension reduction processing to obtain quantum interference feature data, so that the matching precision is improved.

In still other embodiments, S230 may be implemented by a pre-trained model, and specifically, the distribution information may be input into the pre-trained feature extraction model to obtain the quantum interference features. The feature extraction model may be a neural network model capable of outputting quantum interference feature data when the distribution information is input, such as a CNN model, an RNN model, a DNN model, or a modified network of the above networks, and the specific type thereof is not limited.

S240, under the condition that the quantum interference characteristic data meet the preset matching conditions, the candidate data and the data to be matched are determined to be matched.

In the embodiment of the present disclosure, the preset matching condition is a condition that the quantum interference feature needs to satisfy when the candidate data and the data to be matched are matched.

In some embodiments, the preset matching condition includes that the matching degree score corresponding to the quantum interference feature data satisfies the preset score condition.

Correspondingly, fig. 6 shows a schematic flowchart of another cross-modal data matching method provided in the embodiment of the present disclosure. Fig. 6 is different from fig. 2 in that S240 may specifically include S241 and S242.

And S241, inputting the quantum interference characteristic data into a pre-trained matching degree prediction model to obtain a matching degree score of the data to be matched and the candidate data.

In one embodiment, the match prediction model may be a Multilayer Perceptron (MLP). Specifically, the feature vector calculated by the formula (12) may be usedx _att Inputting the data to be matched and the candidate data into a multilayer perceptron, and obtaining the matching degree score S (I, T) of the data to be matched and the candidate data after a series of linear and nonlinear calculations.

Specifically, the multi-layer perceptron may perform the operation steps as shown in equation (13):

S(I,T)=f(x _att ) =2·tanh(W ^T ·x _att +b)（13）

wherein the content of the first and second substances,W ^T in order to be a weight matrix, the weight matrix,bin order to be a vector of the offset,W ^T andbmay be trained.

It should be noted that, in the embodiment of the present disclosure, an RNN model, an LSTM model, or an improved model of each type of prediction model may also be used as the matching degree prediction model, and the specific type of the matching degree prediction model is not limited.

In one example, the training process of the matching degree prediction model may include the following steps B1-B5.

And step B1, acquiring multiple groups of training data, wherein each group of training data comprises corresponding mode sample data to be matched and candidate mode sample data.

In one example, for each set of training data, if the set of training data is a positive sample, the corresponding to-be-matched mode sample data and candidate mode sample data in the set of training data may be to-be-matched mode sample data and candidate mode sample data that are pre-selected and determined to be matched with each other. For example, there may be manually confirmed text-image pairs that match each other.

In another example, for each set of training data, if it is a negative sample, the corresponding to-be-matched modality sample data and candidate modality sample data in the set of training data may be pre-selected and determined unmatched to-be-matched modality sample data and candidate modality sample data. For example, there may be a manually confirmed text-image pair with no match and relatively large differences in the information conveyed.

And step B2, performing quantum interference representation and feature extraction on each group of training data to obtain quantum interference feature data of each group of training data. The specific content of step B2 is similar to that of steps S220-S230, and is not described again here.

And step B3, inputting the respective quantum interference characteristic data of the multiple groups of training data into the matching degree prediction model to be trained to obtain respective matching degree scores of the multiple groups of training data.

The specific content of step B3 is similar to that of S241, and is not described again.

And step B4, judging whether the loss function meets the preset requirement or not based on the matching degree scores of the multiple groups of training data and the matching degree score labels of the multiple groups of training data.

In one example, the match score labels for the sets of training data may be manually preset.

In one example, the Loss function may be a triple Loss (triple Loss) function, and in particular, may be expressed as equation (14):

（14）

wherein the content of the first and second substances,andrepresenting a relevance score for the data to be matched that does not match the candidate modality. Alpha is a hyperparameter. [ x ] of]₊≡ max (x, 0). The goal of model training is to increase S (I, T) as much as possible, letAs small as possible. And finally, updating the parameters of the model by the Adam optimizer.

It should be noted that, in the embodiment of the present disclosure, a contrast Loss (contrast Loss) function, a logistic regression Loss (Softmax Loss) function, a Hinge Loss (Hinge Loss) function, and the like may also be selected and used, which is not limited in particular.

And step B5, under the condition that the loss function does not meet the preset requirement, adjusting model parameters of the matching degree prediction model, and using the re-acquired multiple groups of training data to return to the step B1 to train the adjusted matching degree prediction model until the loss function meets the preset requirement, so as to obtain the trained matching degree prediction model.

Continuing with the previous example, the goal of model training is to increase S (I, T) as much as possible, letAndas small as possible. That is, when S (I, T) is sufficiently large,andand when the time is long enough, obtaining a trained matching degree prediction model.

In one example, in the case where the loss function does not meet the preset requirement, the model parameters of the match degree prediction model may be adjusted using an adam optimizer. Or, a Momentum (Momentum) optimizer, an adadra optimizer, or the like may be selected according to an actual scene or a specific requirement to adjust model parameters of the matching degree prediction model, which is not specifically limited in the embodiment of the present disclosure.

And S242, matching the data to be matched with the candidate data under the condition that the matching degree score meets the preset score condition.

In one example, if the candidate data belongs to the candidate data set, the preset score condition includes: the sorting position of the matching degree score corresponding to the quantum interference characteristic data in the matching degree score corresponding to the candidate to-be-modal data set is smaller than or equal to the preset number. The first preset number may be the first Q matching degree scores corresponding to the candidate to-be-modelled data sets. Wherein, Q can be set according to actual scenes and specific requirements, and is not limited.

The matching degree score corresponding to the candidate to-be-modal data set may be a result of sorting the matching degree scores corresponding to the multiple selectable modal data in the candidate data set according to a descending order. Optionally, the matching degree scores corresponding to the candidate to-be-modelled data sets may be stored in an array form.

Specifically, if the matching degree score S (I, T) corresponding to the quantum interference feature data is arranged between the 1 st bit and the qth bit in the matching degree score corresponding to the candidate to-be-modal data set in descending order, the matching degree score corresponding to the quantum interference feature data is considered to satisfy the preset score condition.

In another example, the matching degree score corresponding to the matching degree characteristic parameter is larger than a preset score threshold value. The score threshold may be preset, or may be a median value, an average value, and the like set according to the matching degree score corresponding to the candidate to-be-modal data set, and a specific setting manner of the score threshold is not limited.

In other embodiments, in addition to the matching methods shown in S241 to S242, the quantum interference feature data may be input into a pre-trained matching model with matching degree score calculation capability and classification capability.

Optionally, the matching model may include a convolutional layer for calculating a matching degree score of the to-be-matched data and the candidate data, and a full connection layer for determining whether the to-be-matched modal data and the candidate modal data match or not according to the matching degree score of the to-be-matched modal data and the candidate modal data. The fully connected layer may be classified based on a classification function such as Softmax function, logistic classification function, etc., which is not particularly limited.

The cross-modal data matching method provided by the embodiment of the disclosure can obtain the distribution information of the data to be matched and the candidate data in the quantum composite system by performing quantization representation on the data to be matched and the cross-modal data, and extract the quantum interference characteristic data between the candidate data and the data to be matched from the distribution information. The quantum interference characteristic data is based on the quantum probability theory and can reflect the cognition of a user on the information commonly expressed by the cross-modal data, so that the candidate data and the data to be matched can be matched from the cognitive level by using the quantum interference characteristic, and the matching precision of the cross-modal information is improved.

In order to facilitate a general understanding of the cross-modal data matching method provided by the embodiment of the present disclosure, taking a news text matching news image as an example, a specific description is provided below for a matching logic of the cross-modal data provided by the embodiment of the present disclosure.

Fig. 7 illustrates a logic diagram of an exemplary cross-modality data matching method provided by an embodiment of the present disclosure. As shown in FIG. 7, for the news text to be published, the BERT model can be used to extract the news text to be publishedlA first data characteristicP ₁ ,P ₂ ,…,P _l . For pictures 1-N in the database, the fast RCNN model can be used to extract each picturemSecond characteristic dataI ₁ ,I ₂ ,…,I _m . For each picture, the cross-modal data matching method provided by the embodiment of the disclosure can be utilized, and the method can be based on the picturemSecond characteristic dataI ₁ ,I ₂ ,…,I _m With news text to be publishedlA first data characteristicP ₁ ,P ₂ ,…,P _l And constructing quantum interference characteristic data of the picture and the news text to be published, and calculating to obtain a matching degree score of the news text to be published and the candidate data based on the quantum interference characteristic data of the news text to be published and the picture. For example, the matching degree score of picture 1S ₁ Picture 2 corresponding matching degree scoreS ₂ Similarly, the picture N corresponds to the matching degree scoreS _N 。

Then, respectively scoring the matching degree of the news text to be published and the pictures 1-NS ₁ -S _N Storing the data into an array, and scoring the degree of match in the arrayS ₁ -S _N And sorting according to the sequence from big to small. If and matching degree scoreS ₁ -S _N The picture sorting results corresponding to the sorting results one-to-one (i.e. the structure of sorting pictures according to the sequence of the matching degree scores corresponding to the pictures from large to small) are shown in fig. 2, fig. N, … …, and picturesiIf the picture with the highest matching degree score is selected to be paired with the news text to be published, the graph 2 can be recommended for the news text to be published.

Fig. 8 shows a flowchart of an exemplary cross-modality data matching method provided by the embodiment of the present disclosure.

In the embodiments of the present disclosure, a desktop calculator, a notebook computer, a cloud server, a server cluster, and other devices or modules having a computing function.

As shown in fig. 8, the matching method across modality data may include the following steps.

And S810, acquiring the data to be matched and the candidate data. The specific content of S810 is similar to that of S210, and is not described again.

S820, respectively extracting the characteristics of the data to be matched and the candidate data to obtain the data to be matchedlOf a first data feature and a candidate datamA second data characteristic. The specific content of S820 is similar to the specific content of step a1, and is not described again.

Exemplarily, through S820, a first data feature set P = last leaf may be extractedP ₁ ,P ₂ ,…,P _l }, and a second set of data features I = &I ₁ ,I ₂ ,…,I _m }。

And S830, constructing a superposition state vector based on the l first data features and the m second data features. The specific content of S830 is similar to the specific content of steps A2-A5, and is not repeated here.

Illustratively, the superposition state vector constructed by step 830 may be as shown in the following equation (1).

And S840, performing density operator operation and trajectory deviation operation on the superposition state vector to obtain a reduced density operator of the superposition state vector. The specific content of S840 is similar to the specific content of S231 to S233, and is not described again.

Illustratively, the reduced density operator calculated by step 840 may be as shown in equation (8) below.

And S850, extracting the effective probability distribution characteristics corresponding to the reduced density operator by using the multi-scale characteristic extraction model. The specific content of S850 is similar to that of S2331, and is not described again.

Illustratively, the effective probability distribution characteristic calculated by step 850 may be as shown in the following equation (10).

And S860, processing the effective probability distribution characteristics by using a text attention mechanism to obtain quantum interference characteristic data. The specific content of S860 is similar to that of S2332, and is not described again.

Illustratively, the quantum interference characteristic data calculated by step 860 may be as shown in the following formula (12).

And S870, inputting the quantum interference characteristic data into a pre-trained matching degree prediction model to obtain a matching degree score of the data to be matched and the candidate data.

The specific content of S870 is similar to the specific content of S241, and is not described again.

Illustratively, the matching degree score calculated by step 870 may be shown as the following formula (13).

And S880, matching the data to be matched with the candidate data under the condition that the matching degree score meets the preset score condition.

The specific content of S880 is similar to the specific content of S242, and is not described again.

The cross-modal data matching method provided by the embodiment of the disclosure can obtain the distribution information of the data to be matched and the candidate data in the quantum composite system by performing quantization representation on the data to be matched and the cross-modal data, and extract the quantum interference characteristic data between the candidate data and the data to be matched from the distribution information. The quantum interference characteristic data is based on the quantum probability theory and can reflect the cognition of a user on the information commonly expressed by the cross-modal data, so that the candidate data and the data to be matched can be matched from the cognitive level by using the quantum interference characteristic, and the matching precision of the cross-modal information is improved.

For convenience of understanding, the embodiment of the present disclosure takes a news release scenario as an example, and specific description is made on the matching method of cross-modal data provided by the embodiment of the present disclosure through fig. 9 to fig. 11.

Fig. 9 is a schematic diagram illustrating a news text to be published according to an embodiment of the present disclosure. As shown in fig. 9, the news content of the news text 901 to be distributed is "9 months and 30 days, in the basketball game played in CC market, team a wins team B with a score of 24:16, and has won … …" of the game. After acquiring the new text to be published 901, a process of image-text matching based on the new text to be published 901 may be as shown in fig. 10.

Fig. 10 is a schematic diagram illustrating the image-text matching between the news text to be published and the news image in the database according to the embodiment of the disclosure. As shown in fig. 10, after the new text to be published 901 is obtained, in order to screen out the new text to be published 901 and the matched news image, the news image 1003 having the highest matching degree with the new text to be published 901 may be screened out from the plurality of news images 1001 and 1003 in the database 1010 by the cross-modal data matching method shown in fig. 2 to 8 according to the embodiment of the present disclosure. Therefore, news pictures related to the written content can be quickly and accurately recommended to the news creators, so that the method is greatly helpful for quickly writing articles with image-text information, and the news publishing efficiency is improved.

Alternatively, if the publishable news information can be automatically generated based on the new text to be published 901 and the matched news image 1003, the generated publishable news information is as shown in fig. 11.

Fig. 11 is a schematic diagram illustrating a publishable news information provided by an embodiment of the present disclosure. As shown in fig. 11, the publishable news information 1101 may include a new text 901 to be published and a news image 1003 interpenetrated in the new text 901 to be published, and the automatically generated publishable news information 1101 may accurately represent news to be published from two dimensions of a graph and a text, so that readability of the publishable news information is improved.

The embodiment of the present disclosure further provides a cross-modal data matching apparatus for implementing the above-mentioned cross-modal data matching method, which is described below with reference to fig. 12.

In the embodiment of the present disclosure, the matching device across modal data may be an electronic device, for example, the multimedia display device may be a device or a module with a computing function, such as a desktop computer, a notebook computer, a cloud server, a server cluster, and the like.

Fig. 12 is a schematic structural diagram illustrating a cross-modal data matching apparatus according to an embodiment of the present disclosure.

As shown in fig. 12, the matching apparatus 1200 for cross-modal data may include a data acquisition unit 1210, a quantization representing unit 1220, a feature extraction unit 1230, and a data matching unit 1240.

A data obtaining unit 1210 configured to obtain data to be matched and candidate data, wherein data modalities of the data to be matched and the candidate data are different;

the quantization representing unit 1220 is configured to perform quantization representation on the data to be matched and the candidate data to obtain distribution information of the data to be matched and the candidate data in a quantum composite system, where the quantum composite system is a quantum system composed of the data to be matched and the candidate data;

a feature extraction unit 1230 configured to extract quantum interference feature data between the data to be matched and the candidate data based on the distribution information;

and the data matching unit 1240 is configured to determine that the candidate data is matched with the data to be matched under the condition that the quantum interference characteristic data meets the preset matching condition.

The cross-modal data matching device of the embodiment of the disclosure can obtain the distribution information of the data to be matched and the candidate data in the quantum composite system by performing quantization representation on the data to be matched and the cross-modal data, and extract the quantum interference characteristic data between the candidate data and the data to be matched from the distribution information. The quantum interference characteristic data is based on the quantum probability theory and can reflect the cognition of a user on the information commonly expressed by the cross-modal data, so that the candidate data and the data to be matched can be matched from the cognitive level by using the quantum interference characteristic, and the matching precision of the cross-modal information is improved.

In some embodiments of the present disclosure, the feature extraction unit 1230 may further include a first calculation subunit and a first feature extraction subunit.

And the first calculating subunit is configured to perform probability distribution calculation on the distribution information to obtain probability density distribution parameters of the data to be matched and the candidate data in the quantum composite system.

And the first feature extraction subunit is configured to perform feature extraction processing on the probability density distribution parameters to obtain quantum interference feature data.

In some embodiments of the present disclosure, a quantum composite system includes a first subsystem comprised of data features of data to be matched and a second subsystem comprised of data features of candidate data.

Accordingly, the feature extraction unit 1230 may further include a second calculation subunit, a third calculation subunit, and a second feature extraction subunit.

And the second calculating subunit is configured to perform probability distribution calculation on the distribution information to obtain probability density distribution parameters of the data to be matched and the candidate data in the quantum composite system.

And the third calculation subunit is configured to perform dimensionality reduction processing on the probability density distribution parameters of the quantum composite system to obtain the probability density distribution parameters of the candidate data in the dimensionality of the second subsystem.

And the second feature extraction subunit is configured to perform feature extraction on the probability density distribution parameters of the dimension of the second subsystem to obtain quantum interference feature data.

Optionally, the second feature extraction subunit may be further configured to:

inputting the probability density distribution parameters of the second subsystem dimension into a pre-trained feature extraction model to obtain the effective probability distribution features of the candidate data;

and processing the effective probability distribution characteristics of the candidate data by using an attention mechanism and the data characteristics of the data to be matched to obtain quantum interference characteristic data.

In some embodiments of the present disclosure, the quantization representation unit 1220 may be further configured to:

acquiring a plurality of first data features of data to be matched and a plurality of second data features of candidate data;

combining any first data characteristic and any second data characteristic to obtain a plurality of characteristic groups;

for each feature group, carrying out tensor product operation processing on the first data features in each feature group and the second data features in each feature group to obtain a quantum composite state vector;

and accumulating the quantum composite state vectors of the plurality of characteristic groups to obtain a superposed state vector for representing distribution information in the quantum composite system.

In some embodiments of the present disclosure, the preset matching condition includes that a matching degree score corresponding to the quantum interference characteristic data satisfies a preset score condition;

accordingly, the data matching unit 1240 may be further configured to:

under the condition that the quantum interference characteristic data meet the preset matching condition, determining that the candidate data are matched with the data to be matched, specifically comprising the following steps of:

inputting the quantum interference characteristic data into a pre-trained matching degree prediction model to obtain a matching degree score of the data to be matched and the candidate data;

and matching the data to be matched with the candidate data under the condition that the matching degree score meets the preset score condition.

Optionally, the candidate data belongs to a candidate data set, and the preset score condition includes:

the sorting position of the matching degree score corresponding to the quantum interference characteristic data in the matching degree score corresponding to the candidate to-be-modal data set is less than or equal to the preset number; or the matching degree score corresponding to the matching degree characteristic parameter is larger than a preset score threshold value.

In some embodiments of the present disclosure, the data to be matched and the candidate data are any one of text data, image data, video data, and audio data, respectively.

It should be noted that the cross-modal data matching apparatus 1200 shown in fig. 12 may perform each step in the method embodiments shown in fig. 2 to fig. 8, and implement each process and effect in the method embodiments shown in fig. 2 to fig. 8, which are not described herein again.

Fig. 13 shows a schematic structural diagram of a cross-modal data matching device according to an embodiment of the present disclosure.

In some embodiments of the present disclosure, the matching device across modal data shown in fig. 13 may be a device or module with a computing function, such as a desktop computer, a notebook computer, a cloud server, a server cluster, and the like.

As shown in fig. 13, the matching device across modal data may include a processor 1301 and a memory 1302 storing computer program instructions.

In particular, the processor 1301 may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured as one or more Integrated circuits implementing embodiments of the present disclosure.

Memory 1302 may include a mass storage for information or instructions. By way of example, and not limitation, memory 1302 may include a Hard Disk Drive (HDD), a floppy Disk Drive, flash memory, an optical Disk, a magneto-optical Disk, tape, or a Universal Serial Bus (USB) Drive or a combination of two or more of these. Memory 1302 may include removable or non-removable (or fixed) media, where appropriate. Memory 1302 may be internal or external to the integrated gateway device, where appropriate. In a particular embodiment, the memory 1302 is non-volatile solid-state memory. In a particular embodiment, Memory 1302 includes Read-Only Memory (ROM). The ROM may be mask-programmed ROM, Programmable ROM (PROM), Erasable PROM (Electrically Erasable PROM, EPROM), Electrically Erasable PROM (Electrically Erasable PROM, EEPROM), Electrically Alterable ROM (Electrically Alterable ROM, EAROM), or flash memory, or a combination of two or more of these, where appropriate.

The processor 1301 performs the steps of the cross-modality data matching method provided by the embodiments of the present disclosure by reading and executing computer program instructions stored in the memory 1302.

In one example, the matching device across modal data may also include a transceiver 1303 and a bus 1304. As shown in fig. 13, the processor 1301, the memory 1302, and the transceiver 1303 are connected via a bus 1304 to complete communication therebetween.

Bus 1304 includes hardware, software, or both. By way of example, and not limitation, a BUS may include an Accelerated Graphics Port (AGP) or other Graphics BUS, an Enhanced Industry Standard Architecture (EISA) BUS, a Front-Side BUS (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) BUS, an InfiniBand interconnect, a Low Pin Count (LPC) BUS, a memory Bus, a Micro Channel Architecture (MCA) Bus, a Peripheral Component Interconnect (PCI) Bus, a PCI-Express (PCI-X) Bus, a Serial Advanced Technology Attachment (SATA) Bus, a Video Electronics Standards Association Local Bus (VLB) Bus, or other suitable Bus, or a combination of two or more of these. Bus 1304 may include one or more buses, where appropriate. Although this disclosed embodiment describes and illustrates a particular bus, this disclosure contemplates any suitable bus or interconnect.

The embodiment of the present disclosure also provides a computer-readable storage medium, which may store a computer program, and when the computer program is executed by a processor, the processor is enabled to implement the cross-modal data matching method provided by the embodiment of the present disclosure.

The storage medium may, for example, include a memory 1302 of computer program instructions executable by the processor 1301 of the cross-modality data matching apparatus to perform the cross-modality data matching method provided by the embodiments of the present disclosure. Alternatively, the storage medium may be a non-transitory computer readable storage medium, for example, the non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a Compact Disc read only Memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.

It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the term "comprises/comprising" is intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.

The foregoing are merely exemplary embodiments of the present disclosure, which enable those skilled in the art to understand or practice the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.