A small sample behavior recognition method and system based on multi-dimensional prototype reconstruction and reinforcement learning

A technology of enhanced learning and recognition methods, applied in the field of computer vision, can solve the problems of weak identification, significant deviation of timing information distribution, and inability to obtain general prototypes, etc., to reduce data deviation, improve discrimination ability, and improve classification accuracy degree of effect
CN114333064BActive Publication Date: 2022-07-26JIANGNAN UNIV
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Patent Information

Authority / Receiving Office
CN ยท China
Patent Type
Patents(China)
Current Assignee / Owner
JIANGNAN UNIV
Publication Date
2022-07-26

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Abstract

The invention relates to a small sample behavior recognition method based on multi-dimensional prototype reconstruction and reinforcement learning. The average prototype of the class, and the re-weighted similarity attention is used to calculate the similarity between the query set sample and the support set sample and the class average prototype, and re-weight the support set sample and the query set sample according to their corresponding similarity, and get two Prototype, the two prototypes are weighted and summed to obtain a cross-enhanced prototype, and a double triplet optimization classification feature space is constructed to enhance the discriminative ability of the cross-enhanced prototype for different categories, and the optimized cross-enhanced prototype is used to identify all categories. The video in the query set sample is classified, which greatly improves the classification accuracy.
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Description

technical field

[0001] The invention relates to the technical field of computer vision, in particular to a small sample behavior recognition method and system based on multi-dimensional prototype reconstruction and reinforcement learning. Background technique

[0002] With the continuous research of machine vision in theory and practice, human behavior recognition has gradually become an important branch. Traditional action recognition methods can be generalized into RGB image-based and video-based methods, but these methods all have serious limitations, namely, they require a large amount of annotated data to train the model to correctly recognize actions, which brings very expensive Calculate the cost. Small-sample learning aims to classify new samples by learning a small number of samples. Small-sample behavior recognition includes two inputs: support set video representation and query set video representation. The model is trained on the support set and uses the support...

Claims

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