Li, X; Xue, S; Xie, J; Yang, X; Ma, Z; Xue, JH; (2025) Interactive triplet attention for few-shot fine-grained image classification. Neurocomputing , 655 , Article 131377. 10.1016/j.neucom.2025.131377.

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Abstract

Few-shot fine-grained classification aims to identify novel fine-grained classes from extremely few examples with ultra-high semantic similarity between classes, hence a notoriously hard task. To extract discriminative features from few samples for recognizing subtle differences between fine-grained classes, it is pivotal to exploit comprehensive interactions across all dimensions in space and channel, which, however, is unexplored yet by state-of-the-art methods in this challenging area. To address this issue, in this paper we show that a simple adjustment to the existing triplet attention module (TAM) can be highly effective for few-shot fine-grained image classification. More specifically, building on TAM which comprises three parallel branches for pairwise interactions between height, width, and channel dimensions, we introduce an additional interaction between the outputs of these three branches, capable of modeling the dependency across all three dimensions; the revised method is dubbed interactive triplet attention module (ITAM). ITAM is a plug-and-play module, which can be inserted into any metric-based few-shot fine-grained image classifiers for performance enhancement. Extensive experiments, on CUB-200-2011, Flowers, Stanford-Cars, and Stanford-Dogs, showcase the superiority of ITAM against state-of-the-art few-shot fine-grained image classifiers.

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