A cross-modal image-text association anomaly detection method
An anomaly detection and cross-modal technology, applied in the direction of neural learning methods, character and pattern recognition, biological neural network models, etc., can solve the problem that it is difficult to fully learn the data association of different modalities, and it is difficult to establish data of different modalities Contact and other issues to achieve the effect of improving accuracy and robustness
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[0027] The present invention provides a cross-modal image-text correlation anomaly detection method, the flowchart of which is as follows figure 1 As shown, it is divided into three stages, namely the image multi-label classification stage, the text multi-label classification stage and the associated anomaly detection stage. details as follows:
[0028] 1. Image multi-label classification stage
[0029] The input of the image multi-label classification stage is the image in the image-text pair to be detected. The image multi-label classification model consists of a CNN encoder and an RNN decoder. The CNN encoder is used to extract important visual features from the image, and then the extracted The features of the input RNN decoder generate a sequence of labels to predict the final label of the image. First, the image is preprocessed. In the preprocessing stage, the size of the image is adjusted to a fixed shape of 288*288, and then the pixels of the three channels of the i...
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