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arXiv Computer Vision By Zhiqi Miao, Estefan\'ia Talavera, Weichuan Zhang, Jiapan Guo

Depth-guided Geometric Adaptation for Few-shot Image Classification

Read the original on arXiv Computer Vision →

The paper introduces DAFIC, a geometry‑guided framework for few‑shot image classification that leverages monocular depth information. DAFIC first learns geometry‑aware token representations via RGB‑D self‑supervised pre‑training, then refines patch representations using a Depth‑guided Geometric Adaptation module that incorporates depth‑derived geometry while suppressing background interference. Finally, Spatial Bidirectional Patch Matching performs dense bidirectional matching on the refined features, reducing sensitivity to spurious correspondences and background clutter. Experiments on four benchmarks demonstrate that DAFIC outperforms existing methods, with ablation and robustness analyses confirming the benefits of coupling geometry‑conditioned episodic adaptation with bidirectional matching.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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