SARE: Sample-wise Adaptive Reasoning for Training-free Fine-grained Visual Recognition

• URL: http://arxiv.org/abs/2603.17729v2
• Date: Tue, 24 Mar 2026 03:19:21 GMT
• Title: SARE: Sample-wise Adaptive Reasoning for Training-free Fine-grained Visual Recognition
• Abstract summary: We propose SARE, a Sample-wise Adaptive textbfREasoning framework for training-free Fine-Grained Visual Recognition.<n>SARE combines fast candidate retrieval with fine-grained reasoning, invoking the latter only when necessary.<n>In the reasoning process, SARE incorporates a self-reflective experience mechanism that leverages past failures to provide transferable discriminative guidance.
• Score: 24.02308757219308
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in Large Vision-Language Models (LVLMs) have enabled training-free Fine-Grained Visual Recognition (FGVR). However, effectively exploiting LVLMs for FGVR remains challenging due to the inherent visual ambiguity of subordinate-level categories. Existing methods predominantly adopt either retrieval-oriented or reasoning-oriented paradigms to tackle this challenge, but both are constrained by two fundamental limitations:(1) They apply the same inference pipeline to all samples without accounting for uneven recognition difficulty, thereby leading to suboptimal accuracy and efficiency; (2) The lack of mechanisms to consolidate and reuse error-specific experience causes repeated failures on similar challenging cases. To address these limitations, we propose SARE, a Sample-wise Adaptive textbfREasoning framework for training-free FGVR. Specifically, SARE adopts a cascaded design that combines fast candidate retrieval with fine-grained reasoning, invoking the latter only when necessary. In the reasoning process, SARE incorporates a self-reflective experience mechanism that leverages past failures to provide transferable discriminative guidance during inference, without any parameter updates. Extensive experiments across 14 datasets substantiate that SARE achieves state-of-the-art performance while substantially reducing computational overhead.

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