CausalFSFG: Rethinking Few-Shot Fine-Grained Visual Categorization from Causal Perspective

• URL: http://arxiv.org/abs/2512.21617v1
• Date: Thu, 25 Dec 2025 10:26:17 GMT
• Title: CausalFSFG: Rethinking Few-Shot Fine-Grained Visual Categorization from Causal Perspective
• Authors: Zhiwen Yang, Jinglin Xu, Yuxin Pen,
• Abstract summary: Few-shot fine-grained visual categorization (FS-FGVC) focuses on identifying various subcategories within a common superclass given just one or few support examples.<n>We propose a new causal FS-FGVC (CausalFSFG) approach inspired by causal inference for addressing biased data distributions.
• Score: 15.302135920904083
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot fine-grained visual categorization (FS-FGVC) focuses on identifying various subcategories within a common superclass given just one or few support examples. Most existing methods aim to boost classification accuracy by enriching the extracted features with discriminative part-level details. However, they often overlook the fact that the set of support samples acts as a confounding variable, which hampers the FS-FGVC performance by introducing biased data distribution and misguiding the extraction of discriminative features. To address this issue, we propose a new causal FS-FGVC (CausalFSFG) approach inspired by causal inference for addressing biased data distributions through causal intervention. Specifically, based on the structural causal model (SCM), we argue that FS-FGVC infers the subcategories (i.e., effect) from the inputs (i.e., cause), whereas both the few-shot condition disturbance and the inherent fine-grained nature (i.e., large intra-class variance and small inter-class variance) lead to unobservable variables that bring spurious correlations, compromising the final classification performance. To further eliminate the spurious correlations, our CausalFSFG approach incorporates two key components: (1) Interventional multi-scale encoder (IMSE) conducts sample-level interventions, (2) Interventional masked feature reconstruction (IMFR) conducts feature-level interventions, which together reveal real causalities from inputs to subcategories. Extensive experiments and thorough analyses on the widely-used public datasets, including CUB-200-2011, Stanford Dogs, and Stanford Cars, demonstrate that our CausalFSFG achieves new state-of-the-art performance. The code is available at https://github.com/PKU-ICST-MIPL/CausalFSFG_TMM.

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