Symmetrical irregular local features for fine-grained visual classification

2022 
can be fully included in the extracted features. In addition, an effective symmetrized local feature extraction module (SLFEM) based on an attention mechanism is proposed to fully use the spatial relationship between the extracted local features and highlight discriminative features. Experiments on six popular fine-grained benchmark datasets: CUB-200-2011, Stanford Dogs, Food-101, Oxford-IIIT Pets, Aircraft and NA-Birds, are conducted to demonstrate the advantages of our proposed method.
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