Multiple Prompt Learning Frameworks

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9 papers in the last four weeks, up 200% on the four weeks before. 0.1% of all new papers.

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Universal Cross-Prompt Adversarial Attacks on Promptable Concept Segmentation
Sep 30, 2026Ziqi Zhou, Yifan Hu, Yufei Song +5Segment Anything Model Black-Box Adversarial Attacks
College of Computer Science, Chongqing University · School of Cyber Science and Engineering, Huazhong University of Science and Technology · School of Computer Science and Technology, Huazhong University of Science and Technology +2
Learning as Deepfakes Evolve: RF-Prompt for Continual Audio Deepfake Detection
Sep 29, 2026Yuankun Xie, Xiaoxuan Guo, Xiaopeng Wang +3Environmental Sound Deepfake Detection Audio Deepfake Detection
The Hong Kong Polytechnic University, Hong Kong SAR, China · Communication University of China, Beijing, China · Beijing Institute of Technology, Beijing, China
UniAR: A Unified Framework for Autism Recognition Enhanced by Multi-View Prompt Learning
Sep 25, 2026Lei Xin, Zeheng Wang, Jiayin Zhu +6Autism Spectrum Disorder Multiple Prompt Learning Frameworks
Wuhan University Wuhan, Hubei, China · Northeast Normal University Changchun, Jilin, China · The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China +4
Hierarchical Prompt Learning for Hyperbolic Vision-Language Models
Sep 21, 2026Andro Erdelez, Pascal Mettes, Behzad BozorgtabarMultiple Prompt Learning Frameworks Visual Embeddings
Aarhus University · University of Amsterdam
PETR: Prompt Ensembling with Training-free Routing for Vision-Language Models
Sep 20, 2026Weihan Cai, Hao Tan, Xinping Gao +2Multiple Prompt Learning Frameworks Vision-Language Model Adaptation
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, and also with the School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China · Purple Mountain Laboratories, Nanjing, China · Beijing University of Posts and Telecommunications, Beijing, China
Preserving Geometric Integrity in Graph Prompting via Measure-Constrained Optimal Transport
Sep 20, 2026Xiangyu Wang, Shuo Wang, Ruiyi Fang +1Multiple Prompt Learning Frameworks Parameter-Efficient Adaptation
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China · Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China · Department of Computer Science, Western University, Canada
Verbalizing Subliminal Learning Effects Using Text Optimization
Sep 15, 2026Nathan Hu, Sanmi Koyejo, Christopher PottsMultiple Prompt Learning Frameworks Dataset Distillation
Stanford University
TecoPrompt: Temporal-Conservative Prompt Learning for Vision-Language Models
Sep 15, 2026Zeyi Shao, Haowen Hua, Jiaxin Zhang +3Multiple Prompt Learning Frameworks Contrastive Language-Image Pre-Training Model
Soochow University, Suzhou, China · NVIDIA, Shanghai, China · Heriot-Watt University Malaysia, Putrajaya, Malaysia +1
PSMP-CLIP: Patch-Prompt SAM and Multi-Semantic Prompting for CLIP-Based Zero-Shot Anomaly Detection
Sep 15, 2026Xuezhi Xiang, Guanghao Wu, Heqi Xiang +4Multimodal Anomaly Detection Few-Shot Segmentation
Information and Communication Engineering, Harbin Engineering University, Harbin, China · Key Laboratory of Advanced Marine Communication and Information Technology, Harbin, China · Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada +1
Low-Rank Prompt Learning for Vision-Language Models with Fixed-Token Bases
Sep 8, 2026Tanvir Muntakim Tonoy, Sajjad Ghiasvand, Mahnoosh Alizadeh +1Multiple Prompt Learning Frameworks Contrastive Language-Image Pre-Training Model
Department of Electrical and Computer Engineering UC Santa Barbara Santa Barbara, CA 93106, USA
SAM3-LoRA: Parameter-Efficient Adaptation of a Concept-Promptable Foundation Model for Multi-Class Structural Defect Segmentation
Aug 31, 2026P. Malaisree, S. Youwai, S. Janrungautai +3Few-Shot Segmentation Wireless Foundation Models
MAA Consultants Co., Ltd. · AI Research Group, Department of Civil Engineering, Faculty of Engineering, King Mongkut´s University of Technology Thonburi (KMUTT). · School of Engineering, University of Phayao. +2
SeCo-SBIR: Semantically Consistent Prompt Learning for Zero-Shot Sketch-Based Image Retrieval
Aug 4, 2026Long Hoang Dang, Tuan Nguyen Huu, Nguyen Minh Hieu +1Zero-Shot Composed Image Retrieval Multiple Prompt Learning Frameworks
Posts and Telecommunications Institute of Technology Ha Noi, Vietnam
PromptPath: Prompt-Adaptive Computational Pathways for In-Context Learning
Aug 3, 2026Hangrui Zhang, Feifei Shao, Yawei Luo +6In-Context Learning Multiple Prompt Learning Frameworks
Zhejiang University, China · University of Nevada, Reno, USA · Guangdong Institute of Intelligence Science and Technology, China
Few-Shot Concept Prompt Learning for Segmentation Foundation Models via Visual Grounding
Aug 3, 2026Rahul Venkataramani, Rachana SathishFew-Shot Segmentation Multiple Prompt Learning Frameworks
Advanced Technology Group, GE HealthCare
A Benchmark Dataset for MLLM-Generated Image Detection: GPT Image2 & Nano Banana2
Aug 2, 2026Zirui Zhang, Yinbo Yu, Donghai Guan +3Ai-Generated Image Detection Image Generation
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China · College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China · School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
U-shaped Multi-granularity Learning for Vision-Language Models
Jul 16, 2026Biao Chen, Yunqian Yu, Xiangxu Zhao +3Recent Vision-Language Models Multiple Prompt Learning Frameworks
University of Electronic Science and Technology of China · Hithink Research, Hangzhou, China
Agentic Context Learning with Self-Discovered Specification
Jul 9, 2026Jike Zhong, Ming Li, Yuxiang Lai +8In-Context Learning Multiple Prompt Learning Frameworks
University of Southern California · University of Florida · 3Emory University +3
When Does Continual Learning Require Learning
Jul 8, 2026Anne Harrington, Nayan Saxena, Michael Murphy +7Continual Learning In-Context Learning
1UC Berkeley · 2Independent · 3Capital Fund Management
SAMPLe: SAM-based Optimizer for Prompt Learning in VLMs
Jul 7, 2026Hossein Rajoli, Fatemeh Lotfi, Niloufar Alipour Talemi +3Multiple Prompt Learning Frameworks Vision-Language Model 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 +2Replay-Based Continual Learning Multiple Prompt Learning Frameworks
1Yonsei University, Seoul, South Korea · 2Samsung Electronics, Suwon, South Korea
CPG-PAD: Concept-Informed Prompts Guided Presentation Attack Detection
Jul 1, 2026Haoyuan Zhang, Xiangyu Zhu, Li Gao +3Multiple Prompt Learning Frameworks Fine-Grained Perception
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
ZEBRA: Zero-Shot Entropy-Regularized Prompt Learning for Base-to-Novel Generalization in Audio-Language Models
Jun 30, 2026Asif Hanif, Mohammad YaqubMultiple Prompt Learning Frameworks Zero-Shot
Mohamed Bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE
TaxoMIL: Taxonomy-Constrained Learning for Hierarchical Whole Slide Image Analysis
Jun 30, 2026Chaeyeon Lee, Khang Nguyen Quoc, Jinsol Song +3Whole-Slide Images Computational Pathology
School of Electrical Engineering, Korea University, Seoul, Republic of Korea · Department of Hospital Pathology, The Catholic University of Korea College of Medicine, Seoul, Republic of Korea
Prompting Diffusion Models for Zero-Shot Instance Segmentation
Jun 21, 2026Irem Zeynep Alagöz, Nils Morbitzer, Andrea Ramazzina +3Few-Shot Segmentation Multiple Prompt Learning Frameworks
Technical University of Munich (TUM) · Munich Center of Machine Learning (MCML) · Mercedes-Benz AG +1
Concept-Constrained Prompt Learning for Few-Shot CLIP Adaptation
Jun 21, 2026Na Sang, Ding Ma, Rui Sang +1Multiple Prompt Learning Frameworks Contrastive Language-Image Pre-Training Model
University of California, San Diego, La Jolla, CA, USA · Georgia Institute of Technology, Atlanta, GA, USA · Independent Researcher
CPS4: Class Prompt driven Semi-Supervised Spine Segmentation with Class-specific Consistency Constraint
Jun 14, 2026Qingtao Pan, Hongzan Sun, Bing Ji +1Multi Organ Computed Tomography Segmentation High Confidence Pseudo-Labels
School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China · Department of Computer and Data Science, Case Western Reserve University, Cleveland, OH 44106, USA · Department of Nuclear Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, China +1
Listen, Look, and Learn: Learning Without Forgetting through SAM-Audio
Jun 9, 2026Avi Gupta, Nilotpal Sinha, Vishnu Raj +4Class-Incremental Learning Catastrophic Forgetting
IIIT Delhi · Dolby Laboratories · IIT Kanpur +1
Interpreting Style Representations via Style-Eliciting Prompts
Jun 4, 2026Junghwan Kim, David JurgensStylistic Fidelity Style
University of Michigan
Subliminal Learning Is Steering Vector Distillation
May 31, 2026Camila Blank, Agam Bhatia, Senthooran Rajamanoharan +2Multiple Prompt Learning Frameworks Steering
Stanford University
One Click per Cell Type Suffices: Training-free Group Interaction for Cell Instance Segmentation
May 28, 2026Sanghyun Jo, Seo Jin Lee, Seohyung Hong +4Cells Multiple Instance Learning
1OGQ, Korea · 2Seoul National University, Korea · 3LG CNS, Korea
Understanding Generalization and Forgetting in In-Context Continual Learning
May 27, 2026Guangyu Li, Meng Ding, Lijie HuIn-Context Learning Replay-Based Continual Learning
1Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) · University of Buffalo
Self-Improving In-Context Learning
May 22, 2026Baturay Saglam, Dionysis KalogeriasIn-Context Learning Multiple Prompt Learning Frameworks
Department of Electrical and Computer Engineering Yale University
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning
May 19, 2026Yuan Zhao, Youwei Pang, Jiaming Zuo +10Multiple Prompt Learning Frameworks Object Localization
1Dalian University of Technology, China · 3Nanyang Technological University, Singapore · 2X3000 Inspection Co., Ltd +3
Easier to Judge than to Find: Predicting In-Context Learning Success for Demonstration Selection
May 18, 2026Haochun Wang, Chaofen Yang, Jiatong Liu +5In-Context Learning Multiple Prompt Learning Frameworks
Research Center for Social Computing and Interactive Robotics, Harbin Institute of Technology, China.
CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts
May 15, 2026Peiyuan Li, Yongqi Huang, Jitao Zhao +3Multiple Prompt Learning Frameworks Graph Representation Learning
School of Computer Science and Technology, Tianjin University, Tianjin, China · Department of Health Technology and Informatics, and Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Training-Inference Consistent Segmented Execution for Long-Context LLMs
May 12, 2026Xianpeng Shang, Jiang Li, Zehua Duo +2Efficient Long-Context Inference LLM Inference Optimization
College of Computer Science, Inner Mongolia University, Hohhot 010021, China · National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian, Hohhot 010021, China · Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology, Hohhot 010021, China +1
When Adaptation Fails: A Gradient-Based Diagnosis of Collapsed Gating in Vision-Language Prompt Learning
May 10, 2026Yunxuan Fang, Ziwei Zhang, Xinhe WangVision-Language Model Adaptation Multiple Prompt Learning Frameworks
Beihang University · Beijing, China
Plug-and-play Class-aware Knowledge Injection for Prompt Learning with Visual-Language Model
May 7, 2026Junhui Yin, Nan Pu, Xinyu Zhang +4Multiple Prompt Learning Frameworks Plug-And-Play
University of Science and Technology Beijing, China. · Hefei University of Technology, China. · University of Auckland, New Zealand. +3
Align3D-AD: Cross-Modal Feature Alignment and Dual-Prompt Learning for Zero-shot 3D Anomaly Detection
May 7, 2026Letian Bai, Xuanming Cao, Juan Du +1Multimodal Anomaly Detection 3D Generation
Smart Manufacturing Thrust, The Hong Kong University of Science and Technology (Guangzhou) · The Hong Kong University of Science and Technology · College of Mechanical and Vehicle Engineering, Hunan University
Active Learning for Conditional Generative Compressed Sensing
May 6, 2026Alexander DeLise, Nick DexterText-Conditioned Diffusion Model Generative Models
Department of Scientific Computing Department of Mathematics Florida State University · Department of Scientific Computing Florida State University
Joint Semantic Token Selection and Prompt Optimization for Interpretable Prompt Learning
May 6, 2026Yating Wang, Yaqi Zhao, Yongshun Gong +2Multiple Prompt Learning Frameworks Automatic Prompt Optimization
School of Software, Shandong University, Jinan, 250101, China
BioVLM: Routing Prompts, Not Parameters, for Cross-Modality Generalization in Biomedical VLMs
Apr 19, 2026Mainak Singha, Tanisha Gupta, Ankit Jha +3Vision-Language Model Adaptation Multiple Prompt Learning Frameworks
University of Trento, Italy · Carnegie Mellon University, USA · LNMIIT Jaipur, India +3
From Adaptation to Generalization: Adaptive Visual Prompting for Medical Image Segmentation
Apr 19, 2026Evren Çetinkaya, Sangmin Lee, Jung Uk Kim +2Semi-Supervised Medical Image Segmentation Multiple Prompt Learning Frameworks
Technical University of Munich · Korea University · Kyung Hee University +1
Generalizable Face Forgery Detection via Separable Prompt Learning
Apr 19, 2026Enrui Yang, Yuezun LiFace Forgery Detection Deepfake Detection
School of Computer Science and Technology, Ocean University of China, Qingdao, China
CI-CBM: Class-Incremental Concept Bottleneck Model for Interpretable Continual Learning
Apr 16, 2026Amirhosein Javadi, Tuomas Oikarinen, Tara Javidi +1Replay-Based Continual Learning Continual Learning
Department of Electrical and Computer Engineering, University of California San Diego · Department of Computer Science and Engineering, University of California San Diego · Halıcıoğlu Data Science Institute, University of California San Diego
Holistic Optimal Label Selection for Robust Prompt Learning under Partial Labels
Apr 8, 2026Yaqi Zhao, Haoliang Sun, Yating Wang +2Multiple Prompt Learning Frameworks Label Propagation
School of Software, Shandong University, Jinan, China
ET-SAM: Efficient Point Prompt Prediction in SAM for Unified Scene Text Detection and Layout Analysis
Mar 26, 2026Xike Zhang, Maoyuan Ye, Juhua Liu +1Segment Anything Model Scene Text Recognition
School of Computer Science, National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, and Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, China
Guided Prompt Evolution for Vision-Language Models Adaptation
Mar 10, 2026Enming Zhang, Jiayang Li, Yanlong Wang +3Vision-Language Model Adaptation Multiple Prompt Learning Frameworks
Tsinghua Shenzhen International Graduate School, Tsinghua University · Sun Yat-sen University · Chinese University of Hong Kong, Shenzhen
Graph Your Own Prompt
Sep 27, 2025Xi Ding, Lei Wang, Piotr Koniusz +1Graph Representation Learning Multiple Prompt Learning Frameworks
Griffith University · Australian National University · Data61/CSIRO +1
CoDoL: Conditional Domain Prompt Learning for Out-of-Distribution Generalization
Sep 18, 2025Min Zhang, Yuyin Wang, Zhongxiang Dai +4Contrastive Language-Image Pre-Training Model Multiple Prompt Learning Frameworks
East China Normal University · Xidian University · Zhejiang Key Laboratory of Artificial Intelligence of Things (AIoT) Network and Data Security +3
VLMGuard: Bootstrapping Malicious Prompt Detectors from Unlabeled Vision-Language Prompts in the Wild
Oct 1, 2024Junlin Fang, Wenyu Chen, Reshmi Ghosh +7Adversarial Prompts Multiple Prompt Learning Frameworks
College of Computing and Data Science Nanyang Technological University · School of Physical and Mathematical Sciences Nanyang Technological University · Microsoft Corp. +1
Prompting with Sign Parameters for Low-resource Sign Language Instruction Generation
Date pendingMd Tariquzzaman, Md Farhan Ishmam, Saiyma Sittul Muna +2Sign Language Translation Sign
Department of Computer Science and Engineering Islamic University of Technology, Bangladesh

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Universal Cross-Prompt Adversarial Attacks on Promptable Concept Segmentation

Ziqi Zhou, Yifan Hu, Yufei Song, Haowen Jiang+4

College of Computer Science, Chongqing University · School of Cyber Science and Engineering, Huazhong University of Science and Technology · School of Computer Science and Technology, Huazhong University of Science and Technology+2

Segment Anything Model Black-Box Adversarial Attacks Multiple Prompt Learning Frameworks Deception

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The Segment Anything Model (SAM) achieves remarkable performance in visual segmentation. The latest SAM3 extends promptable segmentation to concept-level prediction, broadening the scope of segmentation foundation models. While recent works reveal that SAM and SAM2 are vulnerable to adversarial examples, the robustness of SAM3 under the concept segmentation paradigm remains unexplored. In addition, existing adversarial attacks on SAM-series models exhibit limited cross-prompt transferability. To this end, we propose AdvPCS, a universal cross-prompt adversarial attack for Promptable Concept Segmentation (PCS), including a min-max prompt optimization strategy, a global-local perception deception attack, and a temporal transition deviation attack. Specifically, we first identify the hardest-to-attack prompts via min-max bilevel optimization. In the inner maximization, we enhance diversity over candidate point, box, and text prompts. In the outer minimization, we select prompts with the highest responses based on the confidence scores output by the detector. Given the selected prompts, we apply the perception deception attack to minimize both global and local existence probabilities under joint prompting and employ the temporal memory misalignment attack to maximize inter-frame semantic inconsistency and corrupt memory pointers. Extensive experiments on four benchmark datasets show that a single universal adversarial perturbation (UAP) generated by our method generalizes across frames from different videos and achieves strong attack performance under point, box, and text prompts. In particular, it reduces the average mIoU of various PCS models on the SA-CO dataset to below 5% under text prompts, demonstrating strong attack ability.

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