[Submitted on 20 Oct 2026]
Title:Depth-guided Geometric Adaptation for Few-shot Image Classification
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Abstract:Few-shot image classification (FSIC) frequently suffers from visual ambiguity, where background clutter and RGB appearance similarities hinder discriminative feature matching in data-scarce settings. To mitigate this, we propose DAFIC, a geometry-guided framework that incorporates monocular depth into representation learning, episodic adaptation, and dense support-query matching. DAFIC first learns geometry-aware token representations through RGB-D self-supervised pre-training. Subsequently, the Depth-guided Geometric Adaptation (DGA) module uses depth-derived geometry to refine patch representations with geometry-enriched class context while suppressing background interference. Spatial Bidirectional Patch Matching (SBiM) then performs bidirectional dense matching on the refined features, reducing sensitivity to spurious correspondences and background clutter. Extensive experiments on four benchmarks show that DAFIC achieves strong performance against existing methods, with ablation and robustness analyses supporting the effectiveness of coupling geometry-conditioned episodic adaptation with bidirectional matching for few-shot visual recognition.
Comments: This work is accepted by The IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2027
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2610.28856 [cs.CV]
(or arXiv:2610.28856v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.28856
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zhiqi Miao [view email] [v1] Tue, 20 Oct 2026 19:29:42 UTC (9,476 KB)
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