Few-Shot Image Classification

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Overview

Few-shot image classification studies how to identify new visual categories when labeled examples are scarce, a common constraint in specialized or costly-to-annotate settings. Methods include episodic metric learning with prototypes, transfer from pretrained visual and vision-language models, prompt or adapter tuning, and synthetic data augmentation. Recent work also uses unlabeled query images for transductive refinement and applies these approaches to fine-grained, medical, agricultural, and remote-sensing images. A central tension is whether adaptation improves performance without overfitting tiny support sets or losing robustness to domain shift; pretraining overlap and reliable validation remain important concerns.

Latest papers

CardsList

71 papers9 papers in the last four weeks, down 10% on the four weeks before. 0.1% of all new papers.

  1. Hold-One-Shot-Out (HOSO) for Validation-Free Few-Shot CLIP Adapters

    Mar 4, 2026Chris Vorster, Mayug Maniparambil, Noel E. O'Connor +2VLM AdaptationFew-Shot Learning

    ML-Labs, Dublin City University, Dublin, Ireland

  2. XAI and Few-shot-based Hybrid Classification Model for Plant Leaf Disease Prognosis

    Mar 3, 2026Diana Susan Joseph, Pranav M Pawar, Raja Muthalagu +1Plant Disease ClassificationFew-Shot Image Classification

    Department of Computer Science Birla Institute of Technology and Science Pilani, Dubai Campus, Dubai, UAE

  3. Semi-Supervised Few-Shot Adaptation of Vision-Language Models

    Mar 3, 2026Julio Silva-Rodríguez, Ender KonukogluVLM AdaptationFew-Shot Image Classification

    Computer Vision Lab, ETH Zurich, Zurich, Switzerland

  4. ChimeraLoRA: Multi-Head LoRA-Guided Synthetic Datasets

    Feb 23, 2026Hoyoung Kim, Minwoo Jang, Jabin Koo +2Synthetic Data AugmentationDiffusion Model Fine-Tuning

    Graduate School of AI, POSTECH · Dept. of CSE, POSTECH · NAVER AI Lab

  5. Initialization matters in few-shot adaptation of vision-language models for histopathological image classification

    Feb 21, 2026Pablo Meseguer, Rocío del Amor, Valery NaranjoHistopathology Image ClassificationVLM Adaptation

    Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano (HUMAN-Tech), Universitat Politècnica de Valencia, Valencia, Spain · Valencian Graduate School and Research Network for Artificial Intelligence (valgrAI), Valencia, Spain

  6. Visual Species Recognition with Large Multimodal Models as Post-Hoc Correctors

    Dec 10, 2025Tian Liu, Anwesha Basu, James Caverlee +1Large Vision-Language ModelsMultimodal Large Language Models

    Department of Computer Science and Engineering, Texas A&M University · Department of AI, University of Macau

  7. Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples

    Sep 30, 2025Shiyu Wu, Shuyan Li, Jing Li +2Few-Shot LearningAI-Generated Image Detection

    Institute of Automation, Chinese Academy of Sciences, Beijing, China · Beijing Academy of Artificial Intelligence, Beijing, China · University of Chinese Academy of Sciences, Beijing, China +3

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

    Aug 6, 2025Haiyun Guo, Hongyu Guo, Xiangzhao Hao +3Few-Shot Image Classification

    School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China · Foundation Modal Research Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China · Queen Mary School Hainan, Beijing University of Posts and Telecommunications, Beijing 100876, China +1

  9. Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains

    Jul 1, 2025Xin Xu, Eibe Frank, Geoffrey HolmesFew-Shot Domain AdaptationFew-Shot Image Classification

    Department of Computer Science University of Waikato Hamilton, New Zealand

  10. Enabling Validation for Robust Few-Shot Recognition

    Jun 5, 2025Hanxin Wang, Tian Liu, Shu KongFew-Shot LearningFew-Shot Image Classification

    University of Macau · Texas A&M University · Institute of Collaborative Innovation

  11. Mamba-FSCIL: Dynamic Adaptation with Selective State Space Model for Few-Shot Class-Incremental Learning

    Jul 8, 2024Xiaojie Li, Yibo Yang, Jianlong Wu +4Class-Incremental LearningIncremental Learning

    School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China · Pengcheng Laboratory, Shenzhen, China · King Abdullah University of Science and Technology, Jeddah, Saudi Arabia +1