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: An image recognition method and system of convolutional neural network based on global detail supplement, as follows: acquire the image to be recognized and then input it to trained feature extraction network for feature extraction, and obtain features of each stage; learn detail feature according to the image to be tested, and extract the detail feature map; use the self-attention mechanism to fuse the feature map and detail feature map output at the last stage to obtain global detail features; fuse the global detail feature and the features in each stage to obtain the features after global detail supplement; and classify according to the features after global detail supplement, and the category of the maximum value after calculation is the image classification result. The invention constructs a convolution neural network based on global detail supplement, and uses progressive training for image fine granularity classification, further improving fine granularity classification accuracy.

Abstract: The application belongs to the technical field of image segmentation, in particular to an adaptive semi-supervised image segmentation method based on uncertainty knowledge domain and a system thereof, including the following steps: the image to be segmented is acquired; and the image to be segmented is segmented based on the acquired image to be segmented and the preset image segmentation model; wherein, the semi-supervised segmentation model is adopted for the image segmentation model, and the image sample features of the acquired image to be segmented are extracted based on the constructed uncertainty knowledge base. Based on the domain adaptation of feature migration, the extracted image sample features are migrated to the semi-supervised segmentation model, so that the image to be segmented is segmented.

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.

Abstract: The present disclosure provides a multi-task deep Hash learning-based retrieval method for massive logistics product images. According to the idea of multi-tasking, Hash codes of a plurality of lengths can be learned simultaneously as high-level image representation. Compared with single-tasking in the prior art, the method overcomes shortcomings such as waste of hardware resources and high time cost caused by model retraining under single-tasking. Compared with the traditional idea of learning a single Hash code as an image representation and using it for retrieval, information association among Hash codes of a plurality of lengths is mined, and the mutual information loss is designed to improve the representational capacity of the Hash codes, which addresses the poor representational capacity of a single Hash code, and thus improves the retrieval performance of Hash codes.

Abstract: A method for re-recognizing an object image is provided based on multi-feature information capture and correlation analysis weights of an input feature map by using a convolutional layer with a spatial attention mechanism and a channel attention mechanism, causing channel and spatial information to effectively combined, which not only focus on an important feature and suppress an unnecessary feature, but also improve a representation of a feature. A multi-head attention mechanism is used to process a feature after an image is divided into blocks to capture abundant feature information and determine a correlation between features to improve performance and efficiency of object image retrieval. The convolutional layer with the channel attention mechanism and the spatial attention mechanism is combined with a transformer having the multi-head attention mechanism to focus on globally important features and capture fine-grained features, thereby improving performance of re-recognition.