Visual Prompt Learning

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8 papers in the last four weeks, up 167% on the four weeks before. 0.1% of all new papers.

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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
SAMPLe: SAM-based Optimizer for Prompt Learning in VLMs
Jul 7, 2026Hossein Rajoli, Fatemeh Lotfi, Niloufar Alipour Talemi +3Sharpness-Aware Minimization VLM Adaptation
Clemson University, Clemson SC 29634, USA · Siemens Energy (AI Lab), Orlando FL 32826, USA · University of Arizona, Tucson AZ 85721, USA
Learning Probabilistic Prompt for Continual Learning
Jul 6, 2026Hyekang Park, Sanghoon Lee, Geon Lee +2Class-Incremental Learning Prompt Learning
1Yonsei University, Seoul, South Korea · 2Samsung Electronics, Suwon, South Korea
Hidden-Shot: Towards One-Shot Task Generalization for Low-Level Vision Generalist Models
Jul 1, 2026Shao-Jun Xia, Xianzheng Ma, Zichong MengVision Foundation Model Adaptation Few-Shot Learning
Pratt School of Engineering, Duke University, Durham, NC, 27708, USA · Department of Engineering Science, University of Oxford, Oxford, Parks Road, OX1 3PJ, UK · Khoury College of Computer Science, Northeastern University, Boston, MA, 02115, USA
CPG-PAD: Concept-Informed Prompts Guided Presentation Attack Detection
Jul 1, 2026Haoyuan Zhang, Xiangyu Zhu, Li Gao +3VLM Adaptation Domain Generalization
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China · State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China · China Mobile Financial Technology Co., Ltd., Beijing 100032, China
Learning from Reliable Latent Prompts for Visual Recognition with Missing Modalities
Jun 29, 2026Taixi Chen, Nancy GuoMissing-Modality Learning Prompt Learning
School of Computing State University of New York at Binghamton
Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models
Jun 24, 2026Xi Xiao, Xingjian Li, Yunbei Zhang +7Vision Transformer Vision Foundation Model Adaptation
UAB · CMU · Tulane +3
Decoupling Semantics and Geometric Grounding: Spatial Visual Prompts for Language-Conditioned Imitation Learning
Jun 24, 2026Yanzhe Tang, Xinyu Shao, Yuxuan Hu +6Language-Conditioned Robot Manipulation Vision-Language Grounding
1Tsinghua University, Shenzhen, China. · 2Huawei Technologies Co., Ltd., Shenzhen, China. · 3National University of Singapore, Singapore.
BioMedVR: Confusion-Aware Mixture-of-Prompt Experts for Biomedical Visual Reprogramming
Jun 23, 2026Jiaxiang Liu, Tianxiang Hu, Juwei Guan +5VLM Adaptation Medical VLMs
Guangdong Institute of Intelligence Science and Technology, Zhuhai, China · Zhejiang University, Hangzhou, China · Southeast University, Nanjing, China +3
BoxCtrl: 3D-Aware Visual Prompting for Geometric Image Editing
Jun 22, 2026Feifei Wang, Shiyuan Yang, Xiaoyu Li +1Image Editing Visual Prompt Learning
City University of Hong Kong, China · Tencent, China
Bridging Day and Night: Unsupervised Cross-Domain Re-Identification with Synergistic Prompt and Prototype Learning
Jun 10, 2026Jiyang Xu, Rui Liu, Hang DaiUnsupervised Domain Adaptation Prototype Learning
School of Computer Science, Wuhan University, Wuhan, China
Fine-tuning Multi-modal LLMs with ART: Art-based Reinforcement Training
Jun 10, 2026Michal Chudoba, Sergey Alyaev, Petra Galuscakova +1LLM Fine-Tuning Multimodal Large Language Models
University of Stavanger · Stavanger, Norway · NORCE Research +1
Visual Prompting Meets Feature Reconstruction-Based Anomaly Detection with Dual-Teacher Supervision
Jun 8, 2026Mateo Diaz-Bone, Daniel Caraballo, Florian Scheidegger +11Anomaly Localization Reconstruction-Based Anomaly Detection
IBM Research Europe Zurich, Switzerland
Zero-Shot Learning in Industrial Scenarios: New Large-Scale Benchmark, Challenges and Baseline
Jun 6, 2026Zekai Zhang, Qinghui Chen, Maomao Xiong +6VLM Adaptation Industrial Anomaly Detection
School of Control Science and Engineering, Shandong University, Jinan, China · National Supercomputing Center in Jinan, Qilu University of Technology, Jinan, China · College of Intelligence and Computing, Tianjin University, Tianjin, China +2
Unification of Closed-Open Industrial Detection Scenarios: New Large-Scale Benchmarks,Challenges and Baselines
Jun 6, 2026Zekai Zhang, Jinglin Zhang, Qinghui Chen +8Open-Vocabulary Object Detection VLM Adaptation
School of Control Science and Engineering, Shandong University, Jinan 250061, China · Shandong Computer Science Center, Qilu University of Technology, Jinan, China · CEREMADE, University Paris Dauphine, PSL Research University, CNRS, UMR 7534, 75775 Paris, France +4
Geometry-Aware Distillation for Prompt Tuning Biomedical Vision-Language Models
Jun 3, 2026Tran Dinh Tien, Zhiqiang ShenVLM Adaptation VLM Distillation
Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence
Imagine Before You Draw: Visual Prompt Engineering for Image Generation
Jun 3, 2026Liyu Jia, Fengda Zhang, Jiachun Pan +7Conditional Image Generation T2I Generation
1Nanyang Technological University · 2National University of Singapore · 3Zhejiang University +1
Beyond Low-Rank: Low-Rank Sparse Prompting via Spiking Neural Network and Prompt Factorization
Jun 1, 2026Yumiao Zhao, Bo Jiang, Beibei Wang +3Vision Foundation Model Adaptation Low-Rank Adaptation
Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University
Personalize Your Large Vision-language Models With In-context Prompt Tuning
May 29, 2026Yanshu Li, Jiaqian Li, Kuai Yu +4Vision-Language Models Large Vision-Language Models
Brown University · Columbia University · University of Alabama at Birmingham +2
BadBone: Backdoor Attacks Against Backbone Models in Visual Prompt Learning
May 29, 2026Ziqing Yang, Rui Wen, Xinlei He +3Adversarial Attacks Backdoor Attacks
1CISPA Helmholtz Center for Information Security · Institute of Science Tokyo · 3Wuhan University +1
FedMPT: Federated Multi-label Prompt Tuning of Vision-Language Models
May 27, 2026Xucong Wang, Pengkun Wang, Zhe Zhao +3Multi-Label Classification VLM Adaptation
University of Science and Technology of China (USTC) · Suzhou Institute for Advanced Research, USTC · City University of Hong Kong
LV-OSD: Language-Vision-Complementary Open-Set Object Detection
May 27, 2026Yupeng Zhang, Ruize Han, Wei Feng +2Open-Vocabulary Object Detection Object Detection
College of Intelligence and Computing, Tianjin University, Tianjin 300350, China · Faculty of Computer Science and Artificial Intelligence, Shenzhen University of Advanced Technology, Shenzhen 518107, Guangdong, China
Leveraging Text-to-Image Diffusion Models for Unsupervised Visual Object Tracking
May 26, 2026Zhengbo Zhang, Zhigang Tu, Junsong Yuan +2Visual Object Tracking Visual Prompt Learning
Information Systems Technology and Design Pillar, Singapore University of Technology and Design, Singapore · State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, China · Department of Computer Science and Engineering, University at Buffalo, State University of New York, USA +1
HydraPrompt: An Adaptive and Asymmetric Framework of Vision-Language Models for Synthetic Image Detection
May 26, 2026Senyuan Shi, Hao Tan, Zichang Tan +4Image Forgery Detection VLM Adaptation
Beijing University of Posts and Telecommunications · School of Advanced Interdisciplinary Sciences (SAIS), University of Chinese Academy of Sciences · MAIS, Institute of Automation, Chinese Academy of Sciences +2
Image-Conditioned Instance Prompt Network for Referring Remote Sensing Image Segmentation
May 23, 2026Biaoyu Ren, Qingsheng Wang, Cun Xu +2Remote Sensing Image Segmentation Referring Image Segmentation
School of Computer Science, Northwestern Polytechnical University, Xi'an, China · School of Computer Science, Northwestern Polytechnical University, Xi’an, China · College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, China +1
LaCoVL-FER: Landmark-Guided Contrastive Learning Network with Vision-Language Enhancement for Facial Expression Recognition
May 19, 2026Jiaxin Wang, Muwei Jian, Hui Yu +2VLM Adaptation Facial Expression Recognition
School of Airspace Science and Engineering, Shandong University, Weihai, China · School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, China · School of Psychology and Neuroscience, University of Glasgow, Glasgow, U.K. +1
Dual-Prompt CLIP with Hybrid Visual Encoders for Occluded Person Re-Identification
May 19, 2026Zhangjian Ji, Shaotong Qiao, Kai Feng +1Visual Prompt Learning Person Re-Identification
School of Computer & Information Technology, Shanxi University, Wucheng Rd.92, Taiyuan, 030006, Shanxi, China · Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Wucheng Rd.92, Taiyuan, 030006, Shanxi, China
Exposing Functional Fusion: A New Class of Strategic Backdoor in Dynamic Prompt Architectures
May 19, 2026Zeyao Liu, Zhendong Zhao, Xiaojun Chen +3Adversarial Attacks Vision Transformer
Institute of Information Engineering, Chinese Academy of Sciences · State Key Laboratory of Cyberspace Security Defense · School of Cyber Security, University of Chinese Academy of Sciences +1
WOW-Seg: A Word-free Open World Segmentation Model
May 16, 2026Danyang Li, Tianhao Wu, Bin Li +5Image Segmentation Open-Set Recognition
2VCIP, CS, Nankai University · 1NKIARI, Shenzhen Futian · 3AAIS, Nankai University +2
Cluster-Aware Neural Collapse Prompt Tuning for Long-Tailed Generalization of Vision-Language Models
May 12, 2026Boyang Guo, Liang Li, Lin Peng +3Long-Tail Learning VLM Adaptation
Hangzhou Dianzi University, Hangzhou, China · Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China · The Hong Kong Polytechnic University, Hong Kong, China +2

Jul 7, 2026, cs.CVJ/K move · Enter open · S save

Structured-Condensed Prompt Tuning in Vision-Language Models for Fine-grained Image Recognition

Xinda Liu, Qinyu Zhang, Weiqing Min, Guohua Geng+1

School of Information Science and Technology, Northwest University, Xi’an, Shaanxi, China · Laboratory of Intelligent Information Processing, Institute of Computing Technology, Beijing, China

VLM Adaptation Fine-Grained Image Classification Vision-Language Alignment Few-Shot Image Classification Visual Prompt Learning

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Fine-grained image recognition poses a significant challenge due to the substantial expertise and effort required for manual annotation. Vision-language models (VLMs) like CLIP provide a compelling zero-shot alternative, reducing reliance on extensive labeled data. However, their ability to capture subtle distinctions remains limited, leading to subpar recognition performance. While prompt tuning has proven effective for adapting VLMs, most existing methods treat class labels as isolated, discrete entities, overlooking the rich semantic relationships between them. This oversimplified assumption limits the model's ability to capture hierarchical dependencies and inter-class correlations -- both critical for distinguishing visually similar categories. The problem is especially acute in fine-grained classification, where accurate recognition depends on understanding complex label semantics. To address this, we propose Structured-Condensed Prompt Tuning (SCPT), which enhances semantic structure modeling in prompt learning. Specifically, we introduce Semantic Relation Encoding (SRE) to explicitly model inter-class semantic topology and encode structured label relationships. In parallel, we design a Semantic Condensation loss (ScLoss) to suppress redundant supervision and extract discriminative components from the global semantic space. Together, these components significantly improve semantic alignment and fine-grained discrimination. Extensive experiments on 14 fine-grained benchmarks show that SCPT effectively mitigates semantic ambiguity and achieves state-of-the-art performance in both few-shot and base-to-novel generalization settings.

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