Few-Shot Image Classification

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4 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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DVLA-RL++: Dual-Level Vision-Language Alignment with Reinforcement Learning Gating for Few-Shot Learning
Oct 8, 2026Wenhao Li, Xianjing Meng, Qiangchang Wang +3Few-Shot Learning Few-Shot Image Classification
School of Software, Shandong University, Jinan 250101, China · Shenzhen Loop Area Institute, Shenzhen 518038, China · School of Computer and Artificial Intelligence, Shandong University of Finance and Economics, Jinan 250014, China +1
FedSSMCoOp: SSM Encoders for light-weight Federated Prompt Learning for Few-shot Classification
Oct 7, 2026Ankita Das, Ambarish Parthasarathy, Sumohana S. Channappayya +1Few-Shot Image Classification Multimodal Federated Learning
Department of Computer Science and Engineering Indian Institute of Technology Hyderabad · Department of Artificial Intelligence Indian Institute of Technology Hyderabad · Department of Electrical Engineering Indian Institute of Technology Hyderabad
Decoupled and Distilled: Task-Adaptive LoRA-Teachers with Ensemble Knowledge Transfer for Few-Shot Class-Incremental Learning
Sep 30, 2026Hongwei Zhao, Rui Liu, Yansong Liu +2Class-Incremental Learning Low-Rank Adaptation
School of Computer Science and Engineering, Beihang University · School of Computer Science, Beijing University of Posts and Telecommunications
Reprogramming Vision-Language Models via Structured Prompt Reparameterization
Sep 29, 2026Zizhao Li, Chengyi Cai, Mohammed Yaqoob Ansari +3VLM Adaptation Few-Shot Image Classification
The University of Melbourne, Melbourne, Australia
Prompt-Anchored Residual Adaptation for Biomedical Vision-Language Models
Sep 27, 2026Jingxuan Kang, Qianying Yue, Che Liu +1VLM Adaptation Few-Shot Learning
Imperial College London · The Chinese University of Hong Kong
Training-Free Spectral Transductive Refinement for Cross-Domain Few-Shot Classification
Sep 20, 2026Fahim Rahman, S. M. Tanjeeb Meheran Rohan, Md. Taimum Ibne Sayed +2Few-Shot Learning Spectral Methods
Department of Computer Science and Engineering Islamic University of Technology Gazipur, Bangladesh
Whole-Slide Image Analysis under Realistic Few-Shot Annotation Protocols
Aug 31, 2026Tiffanie Godelaine, Maxime Zanella, Karim El Khoury +2Computational Pathology Conditional Random Fields
Institute of Information and Communication Technologies, Electronics, and Applied Mathematics (ICTEAM), Université catholique de Louvain (UCLouvain), 1348 Louvain-la-Neuve, Belgium
When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning
Aug 7, 2026Wei Liu, Xing Deng, Haijian ShaoFew-Shot Domain Adaptation VLM Adaptation
College of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212003, China
Locally Consistent Transductive Information Maximization for Few-Shot Remote Sensing Scene Classification
Jul 31, 2026Karim El Khoury, Benoît Gérin, Benoît Macq +1Remote Sensing Image Understanding Vision Foundation Model Adaptation
ICTEAM, UCLouvain, Louvain-la-Neuve, Belgium
Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning
Jul 24, 2026Haichen Zhou, Yazhe Lyu, Yixiong Zou +2Class-Incremental Learning Incremental Learning
School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
MQAdapter: Multi-Modal Quantum Adapter for Coarse-to-Fine VLM Fine-tuning
Jul 14, 2026Yumiao Zhao, Bo Jiang, Min Lu +2VLM Adaptation Quantum Machine Learning
Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University, Hefei, China · Origin Quantum Computing Company Limited, Hefei, Anhui 230026, China
Answer-Conditioned Chain-of-Thought Distillation for Few-Shot Industrial Vision with Small VLMs
Jul 12, 2026Shubham RaoCoT Distillation VLM Adaptation
Entropy AI Research Labs Private Limited
TCLA: Training-Free Class-wise Logit Adaptation for Medical Vision-Language Models
Jul 10, 2026Tianyou Jiang, Ziyu ZhouFew-Shot Domain Adaptation VLM Adaptation
University of Bern, Bern, Switzerland · Shanghai Jiao Tong University, Shanghai, China
Structured-Condensed Prompt Tuning in Vision-Language Models for Fine-grained Image Recognition
Jul 7, 2026Xinda Liu, Qinyu Zhang, Weiqing Min +2VLM Adaptation Fine-Grained Image Classification
School of Information Science and Technology, Northwest University, Xi’an, Shaanxi, China · Laboratory of Intelligent Information Processing, Institute of Computing Technology, Beijing, China
AdaBoosting Text Prompts for Vision-Language Models
Jul 1, 2026Seokhee Jin, Changhwan Sung, Sunung Mun +2Vision-Language Models Ensemble Learning
KT Corporation, Republic of Korea · Pohang University of Science and Technology (POSTECH), Republic of Korea · National AI Research Lab, Republic of Korea
Decompose, Compare, and Decide: Multimodal LLMs are Implicit Few-Shot Learners
Jun 30, 2026Yunhan Wang, Eshika Khandelwal, Edson Araujo +3Multimodal Large Language Models Few-Shot Prompting
Tuebingen AI Center, University of Tuebingen, Germany
How many labels do you need? A decision framework for cross-habitat marine species recognition
Jun 28, 2026Alzayat Saleh, Mostafa Rahimi AzghadiDomain Generalization Vision Foundation Model Adaptation
College of Science and Engineering, James Cook University, Townsville, QLD, Australia · Centre for AI and Data Science Innovation, James Cook University, Townsville, QLD, Australia
Adaptive Hebbian Memory Routing in Vision Transformers for Few-Shot Learning
Jun 23, 2026Mohammed Yusuf Mujawar, Noorbakhsh Amiri GolilarzHebbian Learning Vision Transformer
Department of Computer Science The University of Alabama, Tuscaloosa, AL, USA
Few-Shot Hyperspectral Aphid Detection via FastGAN Synthetic Data Generation, Transformer-Based Classification and Explainable AI
Jun 19, 2026Ali SaeidanAgricultural Image Analysis Synthetic Data Augmentation
Lancaster Medical School, Faculty of Health and Medicine, Lancaster University, Lancaster LA1 4YG, United Kingdom
GP-Adapter: Gaussian Process CLIP-Adapter for Few-Shot Out-of-Distribution Detection
Jun 5, 2026Taisei Saito, Koretaka Ogata, Takafumi HiroiVLM Adaptation Few-Shot Image Classification
Ricoh Company, Ltd. Ebina, Japan
Optical-Guided Neural Collapse for SAR Few-Shot Class Incremental Learning
Jun 3, 2026Fan Zhang, Sijin Zheng, Fei Ma +4Class-Incremental Learning Remote Sensing
School of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China · School of Information Science and Technology and the Interdisciplinary Research Center for Artificial Intelligence, Beijing University of Chemical Technology, Beijing 100029, China · Beihang University, Beijing 100191, China +1
Explaining is Harder Than Predicting Alone: Evaluating Concept-based Explanations of MLLMs as ICL Visual Classifiers
May 27, 2026Carmen Quiles-Ramírez, Leticia L. Rodríguez, Nicolás Martorell +1VLM Evaluation Multimodal Large Language Models
Universidad de Granada, Spain · Universidad de Buenos Aires, Argentina
Supervised Classification Heads as Semantic Prototypes: Unlocking Vision-Language Alignment via Weight Recycling
May 21, 2026David Méndez, Roberto Confalonieri, Natalia Díaz RodríguezCross-Modal Learning VLM Adaptation
Department of Computer Science and Artificial Intelligence, DaSCI Institute, University of Granada, Granada, Spain · Department of Mathematics “Tullio Levi-Civita”, University of Padova, Padova, Italy.
A Human-in-the-Loop Framework for Efficient Prompt Selection in Microscopy Vision-Language Models
May 19, 2026Abhiram Kandiyana, Ankur Mali, Lawrence O. Hall +2Vision-Language Models Human-in-the-Loop Annotation
University of South Florida, USA · SRC Biosciences, USA
Pretraining Objective Matters in Extreme Low-Data FGVC: A Backbone-Controlled Study
May 15, 2026Alexander Hackett, Srikanth Thudumu, Ginny Fisher +1Contrastive Learning Masked Autoencoders
Santa Clara University · IAAIR
Rethinking the Good Enough Embedding for Easy Few-Shot Learning
May 13, 2026Michael Karnes, Alper YilmazVisual Representation Learning Few-Shot Learning
The Ohio State University, Columbus, OH 43210, USA
A $_3$ B $_2$ : Adaptive Asymmetric Adapter for Alleviating Branch Bias in Vision-Language Image Classification with Few-Shot Learning
May 13, 2026Yiyun Zhou, Zhonghua Jiang, Wenkang Han +4Vision-Language Models VLM Adaptation
1Zhejiang University · 2Swansea University
MC-RFM: Geometry-Aware Few-Shot Adaptation via Mixed-Curvature Riemannian Flow Matching
May 8, 2026Salim Khazem, Ibrahim Mohamed Serouis, Zakaria EzzahedFlow Matching Vision Foundation Model Adaptation
Talan Research Center, Paris, France
Plug-and-play Class-aware Knowledge Injection for Prompt Learning with Visual-Language Model
May 7, 2026Junhui Yin, Nan Pu, Xinyu Zhang +4Prompt Learning VLM Adaptation
University of Science and Technology Beijing, China. · Hefei University of Technology, China. · University of Auckland, New Zealand. +3
Few-Shot Learning Pipeline for Monkeypox Skin Disease Classification Using CNN Feature Extractors
May 6, 2026Md. Safirur Rashid, Sabbir Ahmed, Muhammad Usama Islam +2Skin Lesion Classification Prototype-Based Classification
Department of Computer Science and Engineering, Islamic University of Technology, Gazipur, Bangladesh · Management Information Systems, Metropolitan State University, Minnesota, USA · School of Kinesiology, University of Louisiana at Lafayette, LA, USA

Oct 8, 2026, cs.CVJ/K move · Enter open · S save

DVLA-RL++: Dual-Level Vision-Language Alignment with Reinforcement Learning Gating for Few-Shot Learning

Wenhao Li, Xianjing Meng, Qiangchang Wang, Zhongyi Han+2

School of Software, Shandong University, Jinan 250101, China · Shenzhen Loop Area Institute, Shenzhen 518038, China · School of Computer and Artificial Intelligence, Shandong University of Finance and Economics, Jinan 250014, China+1

Few-Shot Learning Few-Shot Image Classification Vision-Language Alignment RL for VLMs

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Few-shot learning aims to recognize novel categories from limited labeled examples. Recent studies incorporate textual semantics to compensate for limited visual observations and improve class representations. However, high image-text agreement may reflect both intrinsic object properties and incidental context, making support prototypes susceptible to contextual contamination. To address this problem, we propose DVLA-RL++, which extends DVLA-RL with complementary semantic purification (CSP) and counterfactual reinforcement-learning gating (CRG). Specifically, CSP generates intrinsic and nuisance descriptions from labeled supports and compares their agreement with each support token. An ambiguity-dependent rejection margin guides sparse evidence allocation, while an intrinsic semantic anchor fills the unassigned mass to provide a fallback when visual evidence is unreliable. CRG learns layer-wise semantic fusion strengths using a reward that balances recognition performance and nuisance exposure. An independently executed reference trajectory on the same episode provides a paired learning signal. Theoretical analysis relates retained evidence and anchor quality to prototype stability and establishes conditions for unbiased on-policy gradient estimation. Experiments on standard, fine-grained, and cross-domain benchmarks show state-of-the-art accuracy, with an average gain of 1.4% over DVLA-RL. The project page is available at https://peacelwh.github.io/TPAMI27-DVLA-RLpp/.

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