A component-aware, fine-grained classification method and system for small samples

Abstract

This invention discloses a small-sample fine-grained classification method and system based on component perception, relating to the fields of computer vision and intelligent perception technology. The method includes training and inference phases. In the training phase: support sets and query sets are acquired for each sampled category; structural perception and segmentation are performed on the target instance bounding box to generate a key component mask image; global features are extracted and local features are extracted based on the mask image, and fused to obtain target fusion features; feature prototypes are obtained based on the target fusion features in the support set; loss is calculated based on query set features and feature prototypes, and network parameters are updated. In the inference phase: using the trained network, target fusion features of labeled samples of candidate categories are extracted in the same manner, and the average is taken as the feature prototype; target fusion features of the samples to be classified are extracted, and the classification result is output after matching. This invention can effectively capture local discriminative features and improve the generalization ability of small-sample fine-grained classification.

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