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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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
Prompt-Anchored Vision-Text Distillation for Lifelong Person Re-identification
May 6, 2026Wen Wen, Hao Chen, Shiliang ZhangCross-Modal Knowledge Distillation Continual Learning
University of Electronic Science and Technology of China, China · Harbin Institute of Technology, Shenzhen, China · Peking University, China
VL-UniTrack: A Unified Framework with Visual-Language Prompts for UAV-Ground Visual Tracking
May 6, 2026Boyue Xu, Ruichao Hou, Tongwei Ren +1Visual Object Tracking Multi-View Learning
State Key Laboratory for Novel Software Technology, Nanjing University
SpecPL: Disentangling Spectral Granularity for Prompt Learning
May 6, 2026Jingtao Zhou, Xirui Kang, Feiyang Huang +1VLM Adaptation Frequency-Domain Feature Learning
Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR.
UnAC: Adaptive Visual Prompting with Abstraction and Stepwise Checking for Complex Multimodal Reasoning
May 5, 2026Yifan Wang, Yun FuMultimodal Large Language Models Multimodal Reasoning
Northeastern University
Intrinsic Gradient Suppression for Label-Noise Prompt Tuning in Vision-Language Models
May 1, 2026Jiayu Li, Jiaxin Qi, Sheng Zhou +2VLM Robustness VLM Adaptation
EV-CLIP: Efficient Visual Prompt Adaptation for CLIP in Few-shot Action Recognition under Visual Challenges
Apr 24, 2026Hyo Jin Jon, Longbin Jin, Eun Yi KimVLM Adaptation Domain Generalization
Computer Vision Lab., Konkuk University, Seoul, South Korea
Spike-NVPT: Learning Robust Visual Prompts via Bio-Inspired Temporal Filtering and Discretization
Apr 20, 2026Qiugang Zhan, Anning Jiang, Ran Tao +4Vision Foundation Model Adaptation Visual Prompt Learning
Complex Laboratory of New Finance and Economics, Southwestern University of Finance and Economics · Engineering Research Center of Intelligent Finance, Ministry of Education · School of Computer Science and Engineering, 4Laboratory of Intelligent Collaborative Computing, University of Electronic Science and Technology of China +3
From Adaptation to Generalization: Adaptive Visual Prompting for Medical Image Segmentation
Apr 19, 2026Evren Çetinkaya, Sangmin Lee, Jung Uk Kim +2Domain Generalization Medical Image Segmentation
Technical University of Munich · Korea University · Kyung Hee University +1
P3T: Prototypical Point-level Prompt Tuning with Enhanced Generalization for 3D Vision-Language Models
Apr 17, 2026Geunyoung Jung, Soohong Kim, Kyungwoo Song +1Point Cloud Classification VLM Adaptation
Department of Artificial Intelligence, University of Seoul, South Korea · Department of Applied Statistics, Yonsei University, South Korea
DETR-ViP: Detection Transformer with Robust Discriminative Visual Prompts
Apr 16, 2026Bo Qian, Dahu Shi, Xing WeiOpen-Vocabulary Object Detection Detection Transformer
School of Software Engineering, Xi’an Jiaotong University · State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Xi’an Jiaotong University · Work done during the internship at Hikrobot Co., Ltd. +2
Multigrain-aware Semantic Prototype Scanning and Tri-Token Prompt Learning Embraced High-Order RWKV for Pan-Sharpening
Apr 16, 2026Junfeng Li, Wenyang Zhou, Xueheng Li +3Image Enhancement Remote Sensing
Shenzhen Campus of Sun Yat-sen University · Xidian University · University of Science and Technology of China +3
Guided Prompt Evolution for Vision-Language Models Adaptation
Mar 10, 2026Enming Zhang, Jiayang Li, Yanlong Wang +3VLM Adaptation Few-Shot Learning
Tsinghua Shenzhen International Graduate School, Tsinghua University · Sun Yat-sen University · Chinese University of Hong Kong, Shenzhen
MMLoP: Multi-Modal Low-Rank Prompting for Efficient Vision-Language Adaptation
Feb 24, 2026Sajjad Ghiasvand, Haniyeh Ehsani Oskouie, Mahnoosh Alizadeh +1Cross-Modal Learning Prompt Learning
University of California, Santa Barbara · University of California, Los Angeles
Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation
Dec 10, 2025Nadeem Nazer, Hongkuan Zhou, Lavdim Halilaj +2Industrial Anomaly Detection Multimodal Anomaly Detection
Corporate Research, Robert Bosch GmbH, Renningen, Germany · Otto-von-Guericke-University Magdeburg, Magdeburg, Germany · Institute for Artificial Intelligence, University of Stuttgart, Stuttgart, Germany +2
T2T-VICL: Cross-Task Visual In-Context Learning via Implicit Text-Driven VLMs
Nov 20, 2025Shao-Jun Xia, Huixin Zhang, Zhengzhong TuVision-Language Models In-Context Learning
Duke University · Texas A&M University
CoDoL: Conditional Domain Prompt Learning for Out-of-Distribution Generalization
Sep 18, 2025Min Zhang, Yuyin Wang, Zhongxiang Dai +4VLM Adaptation Domain Generalization
East China Normal University · Xidian University · Zhejiang Key Laboratory of Artificial Intelligence of Things (AIoT) Network and Data Security +3
Extending Foundational Monocular Depth Estimators to Fisheye Cameras with Calibration Tokens
Aug 6, 2025Rit Gangopadhyay, Jung-Hee Kim, Xien Chen +3Domain Adaptation Depth Estimation
Yale University1 · Michigan State University2

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

Plug-and-play Class-aware Knowledge Injection for Prompt Learning with Visual-Language Model

Junhui Yin, Nan Pu, Xinyu Zhang, Lingfeng Yang+3

University of Science and Technology Beijing, China. · Hefei University of Technology, China. · University of Auckland, New Zealand.+3

Prompt Learning VLM Adaptation Few-Shot Image Classification Visual Prompt Learning

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Prompt learning has become an effective and widely used technique in enhancing vision-language models (VLMs) such as CLIP for various downstream tasks, particularly in zero-shot classification within specific domains. Existing methods typically focus on either learning class-shared prompts for a given domain or generating instance-specific prompts through conditional prompt learning. While these methods have achieved promising performance, they often overlook class-specific knowledge in prompt design, leading to suboptimal outcomes. The underlying reasons are: 1) class-specific prompts offer more fine-grained supervision compared to coarse class-shared prompts, which helps prevent misclassification of data from different classes into a single class; 2) compared to class-specific prompts, instance-specific prompts neglect the richer class-level information across multiple instances, potentially causing data from the same class to be divided into multiple classes. To effectively supplement the class-specific knowledge into existing methods, we propose a plug-and-play Class-Aware Knowledge Injection (CAKI) framework. CAKI comprises two key components, i.e., class-specific prompt generation and query-key prompt matching. The former encodes class-specific knowledge into prompts from few-shot samples that belong to the same class and stores the learned prompts in a class-level knowledge bank. The latter provides a plug-and-play mechanism for each test instance to retrieve relevant class-level knowledge from the knowledge bank and inject such knowledge to refine model predictions. Extensive experiments demonstrate that our CAKI effectively improves the performance of existing methods on base and novel classes. Code is publicly available at this https URL.

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