CN111651660A - Method for cross-media retrieval of difficult samples - Google Patents
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Abstract
The invention belongs to the technical field of natural language understanding, and discloses a method for cross-media retrieval of difficult samples. The method comprises the following steps: and calculating a fine-grained label representing the correlation size between the text in the text image pair and the text description of the image, and calculating the similarity of the text image pair based on the fine-grained label, thereby realizing the cross-media retrieval of the difficult sample. The method fully utilizes the characteristic that text information contains richer information compared with image information, fully excavates difficult samples in training data, distributes fine-grained labels to the difficult samples according to the difficulty degree, calculates the similarity of the text image pair based on the fine-grained labels, and improves the accuracy of the cross-media retrieval difficult samples.
Description
Technical Field
The invention belongs to the technical field of natural language understanding, and particularly relates to a method for cross-media retrieval of difficult samples.
Background
With the rapid development of internet technology and social media, data in various media forms has exploded. The demand of internet users for information retrieval is gradually increasing. The traditional information retrieval method based on single media cannot meet the requirements of internet users, and users hope to inquire the results of other multiple media types by retrieving media information of one mode. To meet this demand, cross-media information retrieval techniques are receiving increasing attention.
In 2004, hardon et al applied a typical correlation analysis cca (cancer correlation analysis) to the cross-media information retrieval task for the first time. CCA is a linear mathematical model whose main objective is to learn the subspace to maximize the pairwise correlation of two sets of heterogeneous data. After inputting an image/text pair, the CCA measures the similarity between text and image by mapping image and text features to the largest relevant subspace.
In recent years, with the rapid development of deep learning, more and more deep neural network-based cross-media information retrieval models are proposed. The original data set is a positive example of a pair, i.e. a text/image pair representing the same semantic concept. To provide the negative examples required for model training, it is common practice to randomly combine images and text of different semantic concepts to form negative image/text pairs. The model based on the deep neural network generally uses the neural network to extract features of cross-media data, and due to the characteristic of nonlinear mapping, the deep learning model has good expression capability on various complex media data. Dcca (deep CCA) is a non-linear extension of the CCA model used to learn complex non-linear transformations between two types of media data. It constructs a network with a shared layer for data of different media types, which contains two sub-networks, and the output layers are maximally correlated by learning. This method of constructing a data set introduces inevitable problems for the training of the model: the randomly combined negative samples have a large number of simple samples which can be easily and accurately detected by the model, and the samples contribute little to the training of the model. However, there are always some positive and negative examples in the dataset that are easily misclassified, and such examples are called difficult examples. In the process of model training, the influence of a small number of difficult samples which are easily classified wrongly is often ignored because of the influence of a large number of simple samples, so that the model can not converge to a better result and falls into local optimization.
Disclosure of Invention
In order to solve the above problems in the prior art, the present invention provides a method for cross-media searching a difficult sample.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method of retrieving difficult samples across media, comprising the steps of:
step 1, calculating a fine-grained label representing the correlation size between texts in a text image pair and text descriptions of images;
step 1.1, randomly selecting texts and images belonging to the same semantic category from an original data set D of a text image pair to form a positive sample data set
Randomly selecting texts and images belonging to different semantic categories from the D to form a negative sample data set
Wherein the content of the first and second substances,
each text image pair in the step D has the same semantic category; n, J, K number of sample pairs D, P, E, K ═ J;
step 1.2, extracting from D and P
Corresponding text
Composing positive text pairs
Extraction of D from E
Corresponding text
Composing negative text pairs
Computing
And
degree of similarity of
And
degree of similarity of
Step 1.3, calculating a fine-grained label of any text image pair in the positive sample data set P and the negative sample data set E:
step 2, calculating the similarity of the text image pair based on the fine-grained label;
step 2.1, using graph volume model GCN (GraphConvolation)alNetwork) extracting text features v of input text TT；
Step 2.2, extracting image features v of the input image I by using a convolutional Neural network model CCN (convolutional Neural networks)I；
Step 2.3, based on vT、vIConstructing a positive sample data set
And negative sample data set
Q1、Q2The number of sample pairs of the positive sample data set and the negative sample data set respectively; respectively calculating the similarity of the text image pair in the positive sample data set and the negative sample data set
And correcting by using a fine-grained label:
in the formula (I), the compound is shown in the specification,
for the corrected similarity, β is the influence coefficient of the set fine-grained label on the similarity,
calculated according to the formula (1),
calculated according to the formula (2).
Compared with the prior art, the invention has the following beneficial effects:
according to the method, the fine-grained labels representing the correlation size between the text in the text image pair and the text description of the image are calculated, the similarity of the text image pair is calculated based on the fine-grained labels, and cross-media retrieval of difficult samples is achieved. The method fully utilizes the characteristic that text information contains richer information compared with image information, fully excavates difficult samples in training data, distributes fine-grained labels to the difficult samples according to the difficulty degree, calculates the similarity of the text image pair based on the fine-grained labels, and improves the accuracy of the cross-media retrieval difficult samples.
Drawings
Fig. 1 is a schematic diagram of a similarity distribution curve of a text image, wherein the horizontal axis represents similarity and the vertical axis represents logarithm of samples.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
The embodiment of the invention provides a method for cross-media retrieval of difficult samples, which comprises the following steps:
s101, calculating a fine-grained label representing the correlation size between texts in a text image pair and text descriptions of images;
s1011, randomly selecting texts and images belonging to the same semantic category from the original data set D of the text image pair to form a positive sample data set
Randomly selecting texts and images belonging to different semantic categories from the D to form a negative sample data set
Wherein the content of the first and second substances,
each text image pair in the step D has the same semantic category; n, J, K number of sample pairs D, P, E, K ═ J;
s1012, extracting from D and P
Corresponding text
Composing positive text pairs
Extraction of D from E
Corresponding text
Composing negative text pairs
Computing
And
degree of similarity of
And
degree of similarity of
S1013, calculating a fine-grained label of any text image pair in the positive sample data set P and the negative sample data set E:
s102, calculating the similarity of the text image pair based on the fine-granularity label;
s1021, extracting text feature v of input text T by using graph rolling model GCNT；
S1022, extracting image features v of input image I by using convolutional neural network model CCNI；
S1023 based on vT、vIConstructing a positive sample data set
And negative sample data set
Q1、Q2The number of sample pairs of the positive sample data set and the negative sample data set respectively; respectively calculating the similarity of each text image pair in the positive sample data set and the negative sample data set
And correcting by using a fine-grained label:
in the formula (I), the compound is shown in the specification,
for the corrected similarity, β is the influence coefficient of the set fine-grained label on the similarity,
calculated according to the formula (1),
calculated according to the formula (2).
The implementation of the present embodiment is divided into two phases. The first stage is to calculate a fine-grained label of text similarity, which is realized by the step S101; the second phase is to implement cross-modal information retrieval based on fine-grained tags, which is implemented by step S102. The main goal of the first stage is to measure the correlation between the text in the text image pair and the original text description of the image. Text descriptions typically contain richer and more specific information than images. Therefore, the present embodiment represents the image semantics by using the original text description of the image, and judges the difficulty level of the sample by calculating the similarity between the original text and the text in the text image pair. For positive samples, the smaller the similarity, the greater the sample difficulty; for negative samples, the greater the similarity, the greater the sample difficulty.
Step S101 specifically includes S1011 to S1013.
Step S1011 constructs a positive sample data set P and a negative sample data set E based on the original data set D.
Step S1012 extracts the positive text pair and the negative text pair based on D, P, E, and calculates the similarity of each positive text pair and negative text pair, respectively. The similarity adopts cosine similarity.
Step S1013 calculates a fine-grained label of any one text image pair in the positive sample data set P and the negative sample data set E according to the formulas (1) and (2) according to the similarity between each positive text pair and each negative text pair. According to the formulas (1) and (2), the maximum value of the fine-grained label is 1, and the minimum value is 0.
Step S102 specifically includes S1021 to S1023.
Step S1021 extracts a text feature of the input text T using the graph convolution model GCN. The GCN expands the convolution operation into the data of the graph structure, so that the GCN has strong capability of learning local features and fixed features of the graph and is widely applied to a text classification task. In recent research, GCN has demonstrated powerful text semantic modeling and text classification capabilities. In this embodiment, the GCN includes two convolution layers, each convolution layer is followed by a ReLU; the text features are then mapped to the underlying shared semantic space through a fully connected layer.
Step S1022 extracts the image features of the input image I using the convolutional neural network model CCN. CCN is a common model for extracting image features. Pre-trained VGG-19 may also be used to extract image features. For a given one 224 x 224 image, select a 4096-dimensional vector of outputs of the penultimate layer, i.e., FC7 layer, in VGG-19; and then mapped to the potential shared semantic space through a fully connected layer.
And S1023, constructing a positive sample data set and a negative sample data set based on the text features and the image features extracted in the last step, respectively calculating the similarity of each text image pair in the positive sample data set and the negative sample data set, and correcting by using a fine-grained label.
As an alternative embodiment, the Loss function Loss of model learning is:
Loss＝(σ2++σ2-)+λmax(0,m-(μ+-μ-))(5)
in the formula, mu+、σ2+Is composed of
Mean and variance of (d), mu-、σ2-Is composed of
λ is a set proportionality coefficient for adjusting the mean and the variance, and m is a set (μ)+-μ-) The upper limit value of (3).
In this implementationIn the example, in order to reduce the proportion of the model to the identification error of the difficult sample and make the neural network model converge to a better result, the loss function is improved, such as the formulas (5) to (9), and the improved similarity is a value corrected by a fine-grained label. The left curve in fig. 1 represents the similarity distribution of the text image pair of different semantic categories, the right curve represents the similarity distribution of the text image pair of the same semantic category, and the size of the area of the shaded portion reflects the size of the false alarm ratio. The result of minimizing the loss function is to make μ according to equation (5)+At maximum, make mu-、σ2-、σ2+And minimum. From FIG. 1, it is clear that μ-、σ2-、σ2+The smaller, mu+The larger the size, the smaller the area of the shaded portion. Therefore, the shadow area is minimized when the loss function is minimized, and the false alarm rate is reduced. According to the formula (4), after the fine-grained label correction, the similarity of the negative sample pairs is increased, the negative simple samples are increased less, the negative difficult samples are increased more, and the penalty of the negative difficult samples in the learning process is increased, which is equivalent to the right shift of the left curve in fig. 1. Similarly, according to the formula (3), the similarity of the positive sample pairs is reduced, the number of the positive simple samples is reduced, the number of the positive difficult samples is reduced, the penalty of the positive difficult samples is increased in the learning process, and the left shift of the right curve in fig. 1 is equivalent. The area of the shadow part is increased as a result of the left curve moving to the right and the right curve moving to the left, the area of the shadow part is minimized in the learning process, the attention to difficult samples is increased, and the model converges to a better result.
To verify the effectiveness of the present invention, a set of experimental data is given below. The experiment used three data sets, English-Wiki, TVGraz and Chinese-Wiki, respectively, containing 2866, 2360 and 3103 pairs of text images, respectively. The cross-media retrieval is performed on three data sets by using the method and the existing GIN model. The method is different from GIN in that excavation of difficult samples and fine-grained label distribution of samples with different difficulty degrees are added, and the fine-grained labels are added in the calculation process of the loss function, so that the influence of the difficult samples on model learning is enhanced. The results of the experiment are shown in table 1.
TABLE 1 results of the experiment
As can be seen from Table 1, the accuracy of the method of the present invention is significantly better than that of the other models, and about 4%, 3% and 10% increase in English-Wiki, TVGraz and Chinese-Wiki, respectively, compared to GIN. This indicates that the information of sample difficulty degree marked by fine-grained label is helpful to improve the performance of the existing model in the cross-media information retrieval task. Meanwhile, the effectiveness of the method in the task of distributing the fine-grained labels is proved, the introduction of the fine-grained labels enables the learning of the model to pay more attention to the difficult samples, and the model retrieval performance is further improved.
The above description is only for the purpose of illustrating a few embodiments of the present invention, and should not be taken as limiting the scope of the present invention, in which all equivalent changes, modifications, or equivalent scaling-up or down, etc. made in accordance with the spirit of the present invention should be considered as falling within the scope of the present invention.
Claims (2)
1. A method of retrieving difficult samples across media, comprising the steps of:
step 1, calculating a fine-grained label representing the correlation size between texts in a text image pair and text descriptions of images;
step 1.1, randomly selecting texts and images belonging to the same semantic category from an original data set D of a text image pair to form a positive sample data set
Randomly selecting texts and images belonging to different semantic categories from the D to form a negative sample data set
Wherein the content of the first and second substances,
each text image in DPairs all have the same semantic category; n, J, K number of sample pairs D, P, E, K ═ J;
step 1.2, extracting from D and P
Corresponding text
Composing positive text pairs
Extraction of D from E
Corresponding text
Composing negative text pairs
Computing
And
degree of similarity of
And
degree of similarity of
Step 1.3, calculating a fine-grained label of any text image pair in the positive sample data set P and the negative sample data set E:
step 2, calculating the similarity of the text image pair based on the fine-grained label;
step 2.1, extracting text characteristics v of input text T by using graph volume model GCNT；
Step 2.2, extracting image characteristics v of the input image I by using the convolutional neural network model CCNI；
Step 2.3, based on vT、vIConstructing a positive sample data set
And negative sample data set
Q1、Q2The number of sample pairs of the positive sample data set and the negative sample data set respectively; respectively calculating the similarity of the text image pair in the positive sample data set and the negative sample data set
And correcting by using a fine-grained label:
in the formula (I), the compound is shown in the specification,
for the corrected similarity, β is set fine granularityThe influence coefficient of the label on the similarity,
calculated according to the formula (1),
calculated according to the formula (2).
2. The method of retrieving difficult samples across media as claimed in claim 1, wherein the Loss function Loss of model learning is:
Loss＝(σ2++σ2-)+λmax(0,m-(μ+-μ-)) (5)
in the formula, mu+、σ2+Is composed of
Mean and variance of (d), mu-、σ2-Is composed of
λ is a set proportionality coefficient for adjusting the mean and the variance, and m is a set (μ)+-μ-) The upper limit value of (3).
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