UniFGVC: Universal Training-Free Few-Shot Fine-Grained Vision Classification via Attribute-Aware Multimodal Retrieval

• URL: http://arxiv.org/abs/2508.04136v1
• Date: Wed, 06 Aug 2025 07:02:39 GMT
• Title: UniFGVC: Universal Training-Free Few-Shot Fine-Grained Vision Classification via Attribute-Aware Multimodal Retrieval
• Authors: Hongyu Guo, Kuan Zhu, Xiangzhao Hao, Haiyun Guo, Ming Tang, Jinqiao Wang,
• Abstract summary: Few-shot fine-grained visual classification (FGVC) aims to leverage limited data to enable models to discriminate subtly distinct categories.<n>Recent works mostly finetuned the pre-trained visual language models to achieve performance gain, yet suffering from overfitting and weak generalization.<n>We introduce UniFGVC, a universal training-free framework that reformulates few-shot FGVC as multimodal retrieval.
• Score: 36.96113192872342
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot fine-grained visual classification (FGVC) aims to leverage limited data to enable models to discriminate subtly distinct categories. Recent works mostly finetuned the pre-trained visual language models to achieve performance gain, yet suffering from overfitting and weak generalization. To deal with this, we introduce UniFGVC, a universal training-free framework that reformulates few-shot FGVC as multimodal retrieval. First, we propose the Category-Discriminative Visual Captioner (CDV-Captioner) to exploit the open-world knowledge of multimodal large language models (MLLMs) to generate a structured text description that captures the fine-grained attribute features distinguishing closely related classes. CDV-Captioner uses chain-of-thought prompting and visually similar reference images to reduce hallucination and enhance discrimination of generated captions. Using it we can convert each image into an image-description pair, enabling more comprehensive feature representation, and construct the multimodal category templates using few-shot samples for the subsequent retrieval pipeline. Then, off-the-shelf vision and text encoders embed query and template pairs, and FGVC is accomplished by retrieving the nearest template in the joint space. UniFGVC ensures broad compatibility with diverse MLLMs and encoders, offering reliable generalization and adaptability across few-shot FGVC scenarios. Extensive experiments on 12 FGVC benchmarks demonstrate its consistent superiority over prior few-shot CLIP-based methods and even several fully-supervised MLLMs-based approaches.

Related papers

• Vocabulary-free Fine-grained Visual Recognition via Enriched Contextually Grounded Vision-Language Model [52.01031460230826]
  Traditional approaches rely heavily on fixed vocabularies and closed-set classification paradigms.<n>Recent research has demonstrated that combining large language models with vision-language models (VLMs) makes open-set recognition possible.<n>We propose our training-free method, Enriched-FineR, which demonstrates state-of-the-art results in fine-grained visual recognition.
  arXiv  Detail & Related papers  (2025-07-30T20:06:01Z)
• ViLa-MIL: Dual-scale Vision-Language Multiple Instance Learning for Whole Slide Image Classification [52.405499816861635]
  Multiple instance learning (MIL)-based framework has become the mainstream for processing the whole slide image (WSI)<n>We propose a dual-scale vision-language multiple instance learning (ViLa-MIL) framework for whole slide image classification.
  arXiv  Detail & Related papers  (2025-02-12T13:28:46Z)
• LMSeg: Unleashing the Power of Large-Scale Models for Open-Vocabulary Semantic Segmentation [16.864086165056698]
  Existing open-vocabulary approaches leverage vision-language models, such as CLIP, to align visual features with rich semantic features acquired through pre-training on large-scale vision-language datasets.<n>We propose to alleviate the issues by leveraging multiple large-scale models to enhance the alignment between fine-grained visual features and enriched linguistic features.<n>Our method achieves state-of-the-art performance across all major open-vocabulary segmentation benchmarks.
  arXiv  Detail & Related papers  (2024-11-30T05:49:42Z)
• Envisioning Class Entity Reasoning by Large Language Models for Few-shot Learning [13.68867780184022]
  Few-shot learning aims to recognize new concepts using a limited number of visual samples. Our framework incorporates both the abstract class semantics and the concrete class entities extracted from Large Language Models (LLMs) For the challenging one-shot setting, our approach, utilizing the ResNet-12 backbone, achieves an average improvement of 1.95% over the second-best competitor.
  arXiv  Detail & Related papers  (2024-08-22T15:10:20Z)
• Diffusion Feedback Helps CLIP See Better [40.125318318373715]
  Contrastive Language-Image Pre-training (CLIP) excels at abstracting open-world representations across domains and modalities. CLIP has severe visual shortcomings, such as which can hardly distinguish orientation, quantity, color, structure. We present a post-training approach for CLIP models, which largely overcomes its visual shortcomings via a self-supervised diffusion process.
  arXiv  Detail & Related papers  (2024-07-29T17:00:09Z)
• Few-shot Action Recognition with Captioning Foundation Models [61.40271046233581]
  CapFSAR is a framework to exploit knowledge of multimodal models without manually annotating text. Visual-text aggregation module based on Transformer is further designed to incorporate cross-modal-temporal complementary information. experiments on multiple standard few-shot benchmarks demonstrate that the proposed CapFSAR performs favorably against existing methods.
  arXiv  Detail & Related papers  (2023-10-16T07:08:39Z)
• Self-Supervised Open-Ended Classification with Small Visual Language Models [60.23212389067007]
  We present Self-Context Adaptation (SeCAt), a self-supervised approach that unlocks few-shot abilities for open-ended classification with small visual language models. By using models with approximately 1B parameters we outperform the few-shot abilities of much larger models, such as Frozen and FROMAGe.
  arXiv  Detail & Related papers  (2023-09-30T21:41:21Z)
• Delving into Multimodal Prompting for Fine-grained Visual Classification [57.12570556836394]
  Fine-grained visual classification (FGVC) involves categorizing fine subdivisions within a broader category. Recent advancements in pre-trained vision-language models have demonstrated remarkable performance in various high-level vision tasks. We propose a novel multimodal prompting solution, denoted as MP-FGVC, based on the contrastive language-image subcategory (CLIP) model.
  arXiv  Detail & Related papers  (2023-09-16T07:30:52Z)
• Text Descriptions are Compressive and Invariant Representations for Visual Learning [63.3464863723631]
  We show that an alternative approach, in line with humans' understanding of multiple visual features per class, can provide compelling performance in the robust few-shot learning setting. In particular, we introduce a novel method, textit SLR-AVD (Sparse Logistic Regression using Augmented Visual Descriptors). This method first automatically generates multiple visual descriptions of each class via a large language model (LLM), then uses a VLM to translate these descriptions to a set of visual feature embeddings of each image, and finally uses sparse logistic regression to select a relevant subset of these features to classify
  arXiv  Detail & Related papers  (2023-07-10T03:06:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.