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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RE-VLM: Event-Augmented Vision-Language Model for Scene Understanding
May 19, 2026Hanqing Liu, Mingjie Liu, Luoping Cui +3Visual Question Answering Event-Based Vision
School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China · School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China 2State Key Laboratory of General Artificial Intelligence, BIGAI, Beijing, China
iGSP:Implicit Gradient Subspace Projection for Efficient Continual Learning of Vision-Language Models
May 19, 2026Xuezhi Cui, Dongbo Zhou, Wang Guo +8Vision-Language Models VLM Adaptation
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China · Faculty of Artifi cial Intelligence in Education in Central China Normal University, Wuhan 430079, China · School of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
Rotation-Aligned Key Channel Pruning for Efficient Vision-Language Model Inference
May 19, 2026Beomseok Kang, Dongwon Jo, Jiwon Song +2Vision-Language Models Efficient VLM Inference
Seoul National University
EgoBabyVLM: Benchmarking Cross-Modal Learning from Naturalistic Egocentric Video Data
May 18, 2026Dongyan Lin, Phillip Rust, Angel Villar Corrales +19VLM Evaluation Vision-Language Models
Meta Superintelligence Labs · Stanford University · Meta Reality Labs +2
CATA: Continual Machine Unlearning via Conflict-Averse Task Arithmetic
May 18, 2026Shen Lin, Junhao Dong, Rongjie Chen +3VLM Unlearning Vision-Language Models
1Fujian Normal University · 2Nanyang Technological University · 3Xidian University
Starve to Perceive: Taming Lazy Perception in VLMs with Constrained Visual Bandwidth
May 18, 2026Yuhuan Wu, Cong Wei, Fangzhen Lin +2Vision-Language Models VLM Adaptation
Hong Kong University of Science and Technology · University of Waterloo
What's Holding Back Latent Visual Reasoning?
May 18, 2026André G. Viveiros, Nuno Gonçalves, André F. T. Martins +1Vision-Language Models Latent Visual Reasoning
Instituto Superior Técnico, Universidade de Lisboa · Instituto de Telecomunicações · Carnegie Mellon University +1
Self-Evolving Spatial Reasoning in Vision Language Models via Geometric Logic Consistency
May 18, 2026Junming Liu, Yuqi Li, Yifei Sun +6VLM Robustness Vision-Language Models
Shanghai Artificial Intelligence Laboratory · The City University of New York · The Chinese University of Hong Kong +3
CP-MoE: Consistency-Preserving Mixture-of-Experts for Continual Learning
May 18, 2026Yang Liu, Toan Nguyen, Flora D. SalimContinual Learning for LLMs Vision-Language Models
School of Computer Science and Engineering University of New South Wales Australia
CounterCount: A Diagnostic Framework for Counting Bias in Vision Language Models
May 18, 2026Reem Alzahrani, Hassan Alshanqiti, Bushra Bin Hemid +3VLM Evaluation Vision-Language Models
King Abdullah University of Science and Technology, Thuwal, Saudi Arabia. · University of Edinburgh · KAUST
GraSP-VL: Length as a Semantic Granularity Interface for Vision-Language Representations
May 18, 2026Zesheng Li, Chengchang Pan, Honggang QiVision-Language Models Representation Learning
University of the Chinese Academy of Sciences, China
t-gems: text-guided exit modules for decreasing clip image encoder
May 17, 2026Alberto Presta, Grzegorz Stefanski, Michal Byra +1Cross-Modal Learning Vision-Language Models
Samsung AI Center Warsaw · Institute of Fundamental Technological Research, PAS, Warsaw
Medical Context Distorts Decisions in Clinical Vision Language Models
May 17, 2026David Restrepo, Ira Ktena, Maria Vakalopoulou +2VLM Evaluation VLM Robustness
1MICS, CentraleSup´elec - Universit´e Paris-Saclay, France · 2Cancer Data Science Unit, IHU PRISM, National Institute in Precision Oncology, University Paris-Saclay, CentraleSupelec, Gustave Roussy, INSERM, Gif-sur-Yvette · 3Ellison Institute of Technology (EIT), Oxford, UK +1
ChemVA: Advancing Large Language Models on Chemical Reaction Diagrams Understanding
May 17, 2026Mingyang Rao, Kehua Feng, Zhihui Zhu +4Vision-Language Models VLM Reasoning
College of Computer Science and Technology, Zhejiang University Hangzhou, Zhejiang, China · ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University Hangzhou, Zhejiang, China · Department of Chemistry, Fudan University Shanghai, China
Unlocking Dense Metric Depth Estimation in VLMs
May 15, 2026Hanxun Yu, Xuan Qu, Yuxin Wang +2Vision-Language Models Metric Depth Estimation
Zhejiang University · Tencent Hunyuan LLM · HKUST +1
Neutral-Reference Prompting for Vision-Language Models
May 15, 2026Senmao Tian, Xiang Wei, Shunli ZhangVision-Language Models VLM Adaptation
Beijing Jiaotong University, Beijing, China.
KVCapsule: Efficient Sequential KV Cache Compression for Vision-Language Models with Asymmetric Redundancy
May 14, 2026Yingbing Huang, Tharun Adithya Srikrishnan, Steven K. Reinhardt +1Vision-Language Models Efficient VLM Inference
University of Illinois Urbana-Champaign · AMD
Deep Pre-Alignment for VLMs
May 14, 2026Tianyu Yu, Kechen Fang, Zihao Wan +5Vision-Language Models Large Vision-Language Models
1Tsinghua University · 2Taobao & Tmall Group of Alibaba · 3Shanghai Qi Zhi Institute.
MHSA: A Lightweight Framework for Mitigating Hallucinations via Steered Attention in LVLMs
May 14, 2026Wei Ding, Yilin Li, Yudong Zhang +4Vision-Language Models LLM Hallucination Mitigation
Tsinghua University Beijing, China · Tsinghua University, Tencent Beijing, China · Tencent Beijing, China +2
MemLens: Benchmarking Multimodal Long-Term Memory in Large Vision-Language Models
May 14, 2026Xiyu Ren, Zhaowei Wang, Yiming Du +11VLM Evaluation Memory-Augmented VLMs
CSE Deparment, HKUST · CUHK · OmniMemory (Shenzhen) Intelligent Technology Co., Ltd. +1
Mitigating Mask Prior Drift and Positional Attention Collapse in Large Diffusion Vision-Language Models
May 14, 2026Sujung Hong, Chanyong Yoon, Seong Jae HwangVision-Language Models Diffusion Language Model Decoding
Department of Artificial Intelligence, Yonsei University, Seoul, Republic of Korea.
Training Long-Context Vision-Language Models Effectively with Generalization Beyond 128K Context
May 13, 2026Zhaowei Wang, Lishu Luo, Haodong Duan +9Visual Question Answering Vision-Language Models
1CSE Department, HKUST · 2ByteDance Seed · *Work done at ByteDance Seed
SceneGraphVLM: Dynamic Scene Graph Generation from Video with Vision-Language Models
May 13, 2026Vladislav Makarov, Mark Gizetdinov, Dmitry YudinScene Graph Generation Vision-Language Models
MIRAI · MIREA · MIRAI, AXXX
GeoFlowVLM: Geometry-Aware Joint Uncertainty for Frozen Vision-Language Embedding
May 13, 2026Mayank Nautiyal, Li Ju, Andreas Hellander +2Cross-Modal Learning Flow Matching
Department of Information Technology, Uppsala University, Uppsala, Sweden · SciLifeLab, Uppsala University, Uppsala, Sweden
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
Dual-Pathway Circuits of Object Hallucination in Vision-Language Models
May 13, 2026Jiaxin Liu, Ding Zhong, Yue Wang +6Vision-Language Models Object Hallucination in VLMs
UIUC · UMich · Stanford +5
Revealing the Gap in Human and VLM Scene Perception through Counterfactual Semantic Saliency
May 13, 2026Ziqi Wen, Parsa Madinei, Miguel P. EcksteinVLM Evaluation Vision-Language Models
Department of Computer Science, University of California, Santa Barbara · Department of Psychological and Brain Sciences, University of California, Santa Barbara
Lifelong Learning in Vision-Language Models: Enhanced EWC with Cross-Modal Knowledge Retention
May 12, 2026Hamza Ahmed Durrani, Rafay Suleman DurraniVision-Language Models Cross-Modal Alignment
Sejong University, Computer Science Engineering · Technische Universität Ilmenau, Computer Engineering
Revealing Interpretable Failure Modes of VLMs
May 12, 2026Isha Chaudhary, Vedaant V Jain, Kavya Sachdeva +2VLM Robustness Vision-Language Models
UIUC · Kumo AI · IIT Delhi
SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture
May 12, 2026Haiwen Diao, Penghao Wu, Hanming Deng +55Vision-Language Models Unified Multimodal Models

May 19, 2026, cs.CVJ/K move · Enter open · S save

RE-VLM: Event-Augmented Vision-Language Model for Scene Understanding

Hanqing Liu, Mingjie Liu, Luoping Cui, Endian Lin+2

School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China · School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China 2State Key Laboratory of General Artificial Intelligence, BIGAI, Beijing, China

Visual Question Answering Event-Based Vision VLM Robustness Vision-Language Models

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Conventional vision-language models (VLMs) struggle to interpret scenes captured under adverse conditions (e.g., low light, high dynamic range, or fast motion) because standard RGB images degrade in such environments. Event cameras provide a complementary modality: they asynchronously record per-pixel brightness changes with high temporal resolution and wide dynamic range, preserving motion cues where frames fail. We propose RE-VLM, the first dual-stream vision-language model that jointly leverages RGB images and event streams for robust scene understanding across both normal and challenging conditions. RE-VLM employs parallel RGB and event encoders together with a progressive training strategy that aligns heterogeneous visual features with language. To address the scarcity of RGB-Event-Text supervision, we further propose a graph-driven pipeline that converts synchronized RGB-Event streams into verifiable scene graphs, from which we synthesize captions and question-answer (QA) pairs. To develop and evaluate RE-VLM, we construct two datasets: PEOD-Chat, targeting illumination-challenged scenes, and RGBE-Chat, covering diverse scenarios. On captioning and VQA benchmarks, RE-VLM consistently outperforms state-of-the-art RGB-only and event-only models with comparable parameter counts, with particularly large gains under challenging conditions. These results demonstrate the effectiveness of event-augmented VLMs in achieving robust vision-language understanding across a wide range of real-world environments.

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Latest papers 491