Abstract: The present disclosure belongs to the technical field of image processing, and provides a multi-modal image classification system and method using an attention-based multi-interaction network. The present disclosure utilizes a U-net network structure to fuse low-level visual features and high-level semantic features. An attention network is introduced to solve the problem of weak feature discrimination, and high attention is given to discriminative features, so that the attention network plays an important role in the final classification process. A sufficient multi-modal interaction mechanism is introduced, so that more effective correlation information and discriminative information are obtained among a plurality of modalities, and sufficient interaction among the plurality of modalities is completed, thereby solving the problems of weak feature discrimination and insufficient interaction among modalities in a multi-modal image classification task.

Abstract: A multiple scenario-oriented item retrieval method and system. The method includes the steps of extracting, by Hashing learning, image features from an image training set to train a pre-built item retrieval model; when an image is in a scenario of hard samples, introducing an adaptive similarity matrix, optimizing the similarity matrix by an image transfer matrix, constructing an adaptive similarity matrix objective function in combination with an image category label; constructing a loss quantization objective function between the image and a Hash code according to the image transfer matrix; when the image is in a scenario of zero samples, introducing an asymmetric similarity matrix, constructing an objective function by taking the image category label as supervisory information in combination with equilibrium and decorrelation constraints of the Hash code; and training the item retrieval model based on the above objective function to obtain a retrieved result of a target item image.

Abstract: A method and system for rapid retrieval of target images based on artificial intelligence, obtaining a template image and a plurality of known labels corresponding to the template image; extracting an image to be detected from a target image database; inputting both the image to be detected and the template image into a trained convolutional neural network, and outputting a hash code of the image to be detected and a hash code of the template image; obtaining a similarity between the images based on a Hamming distance between the hash codes, then selecting one or more images to be detected with the similarity higher than a set threshold as a retrieval result to output. Accordingly, the method and system is able to better cope with the retrieval of items in complex scenarios.