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The paper proposes a training-free method for few-shot fine-grained image classification. It uses a multimodal LLM to generate attribute-focused captions that distinguish similar classes, then matches image-caption features against category templates, avoiding the overfitting that fine-tuning causes on sparse data. The reported gains are on 12 benchmark datasets, so readers should check how well the approach holds up beyond them, and whether its reliance on caption quality and LLM hallucination control generalizes.
UniFGVC: Universal Training-Free Few-Shot Fine-Grained Vision Classification via Attribute-Aware Multimodal Retrieval
arXiv:2508.04136v4 Announce Type: replace-cross Abstract: Few-shot fine-grained visual classification (FGVC) aims to discriminate between subtly different categories with very limited data. While Multimodal Large Language Models (MLLM) exhibit strong capabilities in both fine-grained visual understanding and open-world category recognition, directly fine-tuning them on few-shot FGVC tasks typically leads to overfitting on sparse samples, which erodes their valuable open-world knowledge and compromises generalization. To fundamentally overcome this limitation, we present UniFGVC, a universal training-free framework that shifts the paradigm from parameter fine-tuning to training-free retrieval-based inference. UniFGVC reframes few-shot FGVC as a multimodal retrieval problem using predefined category templates. Its core is the Category-Discriminative Visual Captioner (CDV-Captioner), an attribute-aware module designed to leverage MLLM for generating structured textual descriptions that explicitly pinpoint discriminative visual attributes between similar classes. By integrating chain-of-thought reasoning and visual references, CDV-Captioner mitigates hallucination and maximizes the discriminative power of textual captions, thereby converting images into attribute-enriched image-description pairs. Subsequently, utilizing arbitrary pre-trained visual and textual encoders in a plug-and-play manner, UniFGVC extracts features from both modalities and computes their similarity with all category templates for recognition. Extensive experiments on 12 FGVC datasets demonstrate UniFGVC's universal compatibility and consistent superiority. It not only outperforms existing few-shot CLIP-based methods but also surpasses several fully-supervised MLLM-based approaches, confirming the general advantage of the proposed retrieval paradigm.
Abstract:Few-shot fine-grained visual classification (FGVC) aims to discriminate between subtly different categories with very limited data. While Multimodal Large Language Models (MLLM) exhibit strong capabilities in both fine-grained visual understanding and open-world category recognition, directly fine-tuning them on few-shot FGVC tasks typically leads to overfitting on sparse samples, which erodes their valuable open-world knowledge and compromises generalization. To fundamentally overcome this limitation, we present UniFGVC, a universal training-free framework that shifts the paradigm from parameter fine-tuning to training-free retrieval-based inference. UniFGVC reframes few-shot FGVC as a multimodal retrieval problem using predefined category templates. Its core is the Category-Discriminative Visual Captioner (CDV-Captioner), an attribute-aware module designed to leverage MLLM for generating structured textual descriptions that explicitly pinpoint discriminative visual attributes between similar classes. By integrating chain-of-thought reasoning and visual references, CDV-Captioner mitigates hallucination and maximizes the discriminative power of textual captions, thereby converting images into attribute-enriched image-description pairs. Subsequently, utilizing arbitrary pre-trained visual and textual encoders in a plug-and-play manner, UniFGVC extracts features from both modalities and computes their similarity with all category templates for recognition. Extensive experiments on 12 FGVC datasets demonstrate UniFGVC's universal compatibility and consistent superiority. It not only outperforms existing few-shot CLIP-based methods but also surpasses several fully-supervised MLLM-based approaches, confirming the general advantage of the proposed retrieval paradigm.
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
arXiv:2508.04136 [cs.CV]
(or arXiv:2508.04136v4 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2508.04136 Focus to learn more arXiv-issued DOI via DataCite
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