Vision-Language Models

Also known as VLM

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55 papers in the last four weeks, up 120% on the four weeks before. 0.5% of all new papers.

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MMArt: A Multi-Perspective Multimodal Dataset for Visual Art Understanding
Aug 11, 2026Shuai Wang, Wangyuan Ding, Yixian Shen +5Image Captioning Vision-Language Models
University of Amsterdam, Amsterdam, The Netherlands · Amazon AGI, United States of America · College of Business and Economics, University of Johannesburg, South Africa
Dynamic Context Adapters: Efficiently Infusing History into Vision-and-Language Models
Aug 11, 2026Yuhang Song, Bor-Jiun Lin, Jiaxu Liu +3Memory-Augmented VLMs Vision-Language Models
Department of Computer Science, University of Liverpool · Department of Computer Science, National Tsinghua University · Imperial College London +1
GRASP: Granularity-Aware Region Alignment and Semantic Prototype Learning for Fine-Grained Cross-Modal Understanding in Drone Views
Aug 10, 2026Jiahui Cui, Yan Zhao, Kan Wei +4Vision-Language Models Fine-Grained Image Retrieval
Aerospace Information Research Institute, Chinese Academy of Sciences · Key Laboratory of Target Cognition and Application Technology (TCAT), Beijing, China · University of Chinese Academy of Sciences, Beijing, China +1
Right Answer, Wrong Heat: Explanation-Aware Evaluation and Thermal-Grounded Feedback for MLLMs on Infrared Images
Aug 10, 2026Yongsong Huang, Xiaofeng Liu, Tomo Miyazaki +2Vision-Language Models Vision-Language Grounding
Tohoku University · Yale University
Real Data Closes Synthetic-to-Real Gap in Optical Chemical Structure Recognition
Aug 10, 2026Yani Guan, Dengpan Dong, Zi Wei +4Synthetic-to-Real Domain Adaptation Vision-Language Models
SES AI Corporation
TomaMMU: A Comprehensive Multimodal Understanding Benchmark for Tomato Leaf Diseases
Aug 9, 2026Gia-Han Truong, Khang Nguyen Quoc, Luyl-Da QuachVision-Language Models Plant Disease Classification
Department of Information Technology, FPT University, Can Tho, Vietnam · School of Electrical Engineering, Korea University, Seoul, South Korea
VLZip: Unified Visual and Textual Compression for Interleaved Long-Context Modeling
Aug 9, 2026Yuqi Zhang, Cheng Chen, Yuyu Guo +6Vision-Language Models Efficient VLM Inference
Shanghai Key Lab of Intell. Info. Processing, School of CS, Fudan University, China · Shanghai Innovation Institute, China · Ant Group, China
VectraYX-Vision-1B: A Sub-2B Spanish/LATAM Cybersecurity Vision-Language Model with Structured Visual Reasoning and Native Tool Use
Aug 9, 2026Juan S. SantillanaVLM Evaluation Tool Use in VLMs
Globant
Wiener Representation Filtering for VLM Hallucination Suppression
Aug 8, 2026Ameen Ali, Tamim Zoabi, Lidor Brami +1Vision-Language Models Object Hallucination in VLMs
Tel Aviv University
LHSDet: High-Resolution AI-Generated Image Detection via Visual Question Answering
Aug 8, 2026Qian Yao, Jun-Jie Huang, Yongjun Wang +1Vision-Language Models AI-Generated Image Detection
College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, China · Academy of Military Science, Beijing 100091, China
Jako Tako or Fluent? Presenting PoVisLE: A Polish Vision-Language Evaluation
Aug 7, 2026Anna Kołos, Grzegorz Statkiewicz, Karolina Seweryn +3VLM Evaluation Visual Question Answering
NASK National Research Institute, Warsaw, Poland
SABRE: Scalable and Automated Benchmarking of VLMs under Stress
Aug 7, 2026Zixuan Lan, Luzhe Sun, Matthew R. Walter +1VLM Evaluation VLM Robustness
University of Chicago · Stony Brook University · Toyota Technological Institute at Chicago
A Picture is Worth a Thousand Tokens: How Vision Language Models Cut AI Energy Costs While Improving Accuracy
Aug 7, 2026Bhavika Jalli, Nikhil Korati Prasanna, Jayanta ChoudhuryVision-Language Models Efficient VLM Inference
Ericsson
Same Attention, Different Truths: Put Logit-Lens over Visual Attention to Detect and Mitigate LVLM Object Hallucination
Aug 7, 2026Zichuan Wang, Songlin Yang, Bo Peng +4Hallucination Detection in VLMs Vision-Language Models
School of Artificial Intelligence, University of Chinese Academy of Sciences · 3Hong Kong University of Science and Technology · 2New Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Science
Gaze Behavior in Visual World Experiments Can be Modeled With Off-the-shelf Language-Vision Encoders
Aug 7, 2026Rahul Murali Shankar, Titus von der Malsburg, Sebastian PadóVision-Language Models Computational Psycholinguistics
Computational Linguistics · University of Stuttgart, Germany · Linguistics
Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models
Aug 7, 2026Minseok Kang, Hyunwoo Kim, Chanyoung Kim +3Vision-Language Models Efficient VLM Inference
Department of Artificial Intelligence, Chung-Ang University, Seoul, Korea · School of Computer Science and Engineering, Soongsil University, Seoul, Korea · Department of Computer Science, Kookmin University, Seoul, Korea
Consistency Has a Computable Blind Spot: A Commutation Theory of Label-Free Reliability for Vision-Language Figure Reading
Aug 6, 2026Rasul Khanbayov, Hasan KurbanVLM Evaluation Vision-Language Models
College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
DynaPix: Can Vision-Language Models Identify the Exact Future?
Aug 6, 2026Thong Nguyen, Vinh-Hien Do, Quynh Vo +2VLM Evaluation Vision-Language Models
Centre for AI Research, VinUniversity · National University of Singapore
Unleashing the Potential of Vision-Language Models for Generalizable AI-Generated Image Detection
Aug 5, 2026Weihan Cai, Hao Tan, Zichang Tan +2Prototype-Based Classification Vision-Language Models
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China · School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China · Sangfor Technologies Inc., Shenzhen 518055, China +1
Evaluating the Diagnostic Robustness of Vision-Language Models Under Visual and Textual Perturbations
Aug 5, 2026Ali Khoramfar, Mohammad Javad Dousti, Alireza Mohamadian +1VLM Evaluation VLM Robustness
Department of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran · Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran
Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling
Aug 5, 2026Han Chen, Ming Li, Hong Jiao +1Vision-Language Models Educational Assessment
Mohamed bin Zayed University of Artificial Intelligence · University of Maryland
GeoReward: Mitigating Contextual Variable Overestimation in Vision-Language Models for Cross-Market Preference Prediction
Aug 5, 2026Shuo Liu, Huixiang Cai, Weiru Zhang +1Vision-Language Models VLM Adaptation
Alibaba International Digital Commerce Group
Beyond Global Routing Aggregation: Phase-Aware Expert Merging for MoE Vision-Language Models
Aug 5, 2026Hongyu Zhang, Cheng Yan, Xiang Xia +1Vision-Language Models Mixture of Experts
University of Science and Technology of China, Hefei, China
ParVL: Parallel Scaling and Expandable Compute Allocation for Multimodal LLMs
Aug 4, 2026Yang Yang, Qinyu Zhao, Mouxiang Chen +5Vision-Language Models Multimodal Large Language Models
1The Australian National University · 2Shanghai AI Laboratory · 3Zhejiang University +2
MuRA: Multi-Rank Adaptation for Efficient and Effective Test-Time Vision-Language Generalization
Aug 4, 2026Gengyuan Liu, Nanzhou Wang, Chang Liu +5Vision-Language Models VLM Adaptation
Tsinghua University Shenzhen, China · Tsinghua University Beijing, China · Huazhong University of Science and Technology Wuhan, China +1
UHP Detection: LVLMs have their Unique Hallucination Pattern in the Consistency Space
Aug 4, 2026Amir Mohammad Ezzati, Kiyan Rezaee, Bardiya Kariminia +4Hallucination Detection in VLMs Vision-Language Models
Sharif University of Technology · Michigan State University · Shahid Beheshti University +2
Can Text-to-Image Models Draw from the Right Frame of Reference?
Aug 4, 2026Zheyuan Gu, Ruihang Li, Yong Huang +5Vision-Language Models T2I Generation
1ERNIE Team, Baidu Inc. · 2Peking University
In-Context Collapse in Vision-Language Models and How to Mitigate it?
Aug 3, 2026Mohammad RostamiVLM Robustness Vision-Language Models
Amazon Generative AI Innovation Center∗, USA.
HAFI-VLM: A Frequency Perspective for Diagnosing and Enhancing Visual Perception in Vision-Language Models
Aug 3, 2026Jin Cui, Chuanchang Su, Jiayi Lu +3Visual Question Answering VLM Robustness
1State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, and Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University · School of Computer Science and Technology, Xi’an Jiaotong University · School of Software Engineering, Xi’an Jiaotong University
TALSC: Timeliness-Aware Large-Small VLM Collaboration for Infrastructure-Assisted Autonomous Driving
Aug 3, 2026Mengmeng Zhu, Yuxuan Sun, Wei Chen +1Vision-Language Models VLMs for Autonomous Driving
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China

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MMArt: A Multi-Perspective Multimodal Dataset for Visual Art Understanding

Shuai Wang, Wangyuan Ding, Yixian Shen, Jia-Hong Huang+4

University of Amsterdam, Amsterdam, The Netherlands · Amazon AGI, United States of America · College of Business and Economics, University of Johannesburg, South Africa

Image Captioning Vision-Language Models

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Recent vision-language models demonstrate impressive general visual understanding, yet their art interpretation remains shallow: they describe surface content but struggle with formal analysis, grounded historical interpretation, or affective characterization. We argue this is not only a model but also a dataset limitation. Existing art datasets are single perspective resources, where no dataset provides narrative, formal, emotional, and historical perspectives simultaneously for the same artworks. We introduce MMArt, a large-scale dataset of 74,234 WikiArt paintings, each annotated with four independently annotated perspectives plus a harmonized unified caption, produced by specialized vision-language models or human annotation and validated through complementary quality evaluations. Two complementarity analyses establish that perspectives encode genuinely distinct information. A generative analysis shows that formal analysis descriptions best preserve compositional style, and historical descriptions carry strong affective signal in reconstructed images. A discriminative retrieval analysis reveals task-asymmetry: narrative descriptions drive retrieval (R@1 = 44.0%), while formal descriptions, strongest for reconstruction, are nearly nondiscriminative at retrieval scale (R@1 = 7.8%). Leave-one-out analysis further confirms that historical descriptions are the least replaceable perspective across both tasks. Together, the two analyses establish that no single perspective suffices for all tasks, directly motivating MMArt multi-perspective design. The dataset, code, and additional information are available at https://shuaiwang97.github.io/MMArt/.

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