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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BUZZY: Contrastive Scoring to Mitigate Text-Induced Bias in Multimodal Multiple-Choice QA
Mar 30, 2026Taeyun Roh, Suhyeong Park, Dongyoung Lee +4Vision-Language Models Multimodal QA
Korea University · AIGEN Sciences · KAIST +1
Uncertainty-guided Compositional Alignment with Part-to-Whole Semantic Representativeness in Hyperbolic Vision-Language Models
Mar 23, 2026Hayeon Kim, Ji Ha Jang, Junghun James Kim +1Vision-Language Models Hierarchical Representation Learning
Dept. of Electrical and Computer Engineering, Seoul National University, Republic of Korea · INMC & IPAI, Seoul National University, Republic of Korea
ME-IQA: Memory-Enhanced Image Quality Assessment via Re-Ranking
Mar 21, 2026Kanglong Fan, Tianhe Wu, Wen Wen +6Memory-Augmented VLMs Vision-Language Models
City University of Hong Kong, Hong Kong SAR, China · ByteDance Inc., China
FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification
Mar 18, 2026Qingran Lin, Fengwei Yang, Chaolun ZhuVision-Language Models Synthetic Data Generation
Georgia Institute of Technology · Duke University · Waseda University
Explicit Logic Channel for Validation and Enhancement of MLLMs on Zero-Shot Tasks
Mar 12, 2026Mei Chee Leong, Ying Gu, Hui Li Tan +2VLM Evaluation Vision-Language Models
Institute for Infocomm Research (I2R) · Present address: Institute of Advanced Intelligence and Computing (IAIC), Agency for Science, Technology and Research (A*STAR), Singapore
GreenRFM: Learning a resource-efficient radiology vision-language foundation model via supervision-centric pre-training
Mar 6, 2026Yingtai Li, Shuai Ming, Qiuli Wang +13Medical Imaging Foundation Models Radiology VLMs
School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China (USTC), Hefei, Anhui, 230026, China · Center for Medical Imaging, Robotics, Analytic Computing & Learning (MIRACLE), Suzhou Institute for Advanced Research, USTC, Suzhou, Jiangsu, 215123, China · Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, USTC, Hefei, Anhui, 230001, China +7
VisNec: Measuring and Leveraging Visual Necessity for Multimodal Instruction Tuning
Mar 1, 2026Mingkang Dong, Hongyi Cai, Jie Li +4Vision-Language Models Data Selection
SJTU · Universiti Malaya · CMU +3
How to Train Your Long-Context Visual Document Model
Feb 16, 2026Austin VeselkaVisual Question Answering Vision-Language Models
LightOn
Robustness of Vision Language Models Against Split-Image Harmful Input Attacks
Feb 8, 2026Md Rafi Ur Rashid, MD Sadik Hossain Shanto, Vishnu Asutosh Dasu +1VLM Robustness Adversarial Attacks on VLMs
Pennsylvania State University · Bangladesh University of Engineering and Technology
Same Answer, Different Representations: Hidden instability in VLMs
Feb 6, 2026Farooq Ahmad Wani, Alessandro Suglia, Rohit Saxena +6VLM Evaluation VLM Robustness
Sapienza University of Rome · University of Edinburgh · University of Oxford +5
Model Specific Task Similarity for Vision Language Model Selection via Layer Conductance
Feb 1, 2026Wei Yang, Hong Xie, Tao Tan +3VLM Evaluation Vision-Language Models
School of AI and Data Science USTC
Gated Relational Alignment via Confidence-based Distillation for Efficient VLMs
Jan 30, 2026Yanlong Chen, Amirhossein Habibian, Luca Benini +1Cross-Modal Knowledge Distillation Vision-Language Models
Department of Information Technology and Electrical Engineering, ETH Zurich, Zurich, Switzerland · School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore · Qualcomm AI Research, Amsterdam, the Netherlands +1
LightOnOCR: A 1B End-to-End Multilingual Vision-Language Model for State-of-the-Art OCR
Jan 20, 2026Said Taghadouini, Adrien Cavaillès, Baptiste AubertinVision-Language Models Optical Character Recognition
LightOn
From One-to-One to Many-to-Many: Dynamic Cross-Layer Injection for Deep Vision-Language Fusion
Jan 15, 2026Cheng Chen, Yuyu Guo, Pengpeng Zeng +4Vision-Language Models VLM Adaptation
Tongji University, China · Ant Group, China · Shanghai Innovation Institute, China +1
Zero-Shot Distracted Driver Detection via Vision Language Models with Double Decoupling
Jan 13, 2026Takamichi Miyata, Sumiko Miyata, Andrew MorrisVision-Language Models Autonomous Driving Perception
Chiba Institute of Technology · Chiba, Japan · Institute of Science Tokyo +3
CoMa: Contextual Massing Generation with Vision-Language Models
Jan 13, 2026Evgenii Maslov, Alexandra Vabnits, Vladimir Vorona +6Vision-Language Models Multimodal Generation
FusionBrain Lab, Artificial Intelligence Research Institute, 123112 Moscow, Russia · Research Center of the Artificial Intelligence Institute, Innopolis University, 420500 Innopolis, Russia · ITMO University, Institute of Design and Urban Studies, 197101 Saint Petersburg, Russia +2
UniDrive-WM: Unified Understanding, Planning and Generation World Model for Autonomous Driving
Jan 7, 2026Zhexiao Xiong, Xin Ye, Burhan Yaman +5Vision-Language Models World Model Learning
Bosch Research North America · Bosch Center for Artificial Intelligence (BCAI) · Arizona State University +2
Parameter Efficient Multimodal Instruction Tuning for Romanian Vision Language Models
Dec 16, 2025George-Andrei Dima, Răzvan-Alexandru Smădu, Dumitru-Clementin CercelVisual Question Answering Image Captioning
National University of Science and Technology POLITEHNICA Bucharest, Bucharest, Romania
Improving Semantic Uncertainty Quantification in LVLMs with Semantic Gaussian Processes
Dec 16, 2025Joseph Hoche, Andrei Bursuc, David Brellmann +4Vision-Language Models Uncertainty Quantification
AMIAD, Pôle Recherche, Palaiseau · Safran Tech · University of Liège +3
SpaceDrive: Infusing Spatial Awareness into VLM-based Autonomous Driving
Dec 11, 2025Peizheng Li, Zhenghao Zhang, David Holtz +6Vision-Language Models VLMs for Autonomous Driving
Mercedes-Benz AG · TU Munich · Karlsruhe Institute of Technology +4
How (Mis)calibrated is your Federated CLIP and what to do about it?
Dec 3, 2025Mainak Singha, Masih Aminbeidokhti, Paolo Casari +3Vision-Language Models VLM Adaptation
University of Trento, Italy · ´Ecole de technologie sup´erieure, QC, Canada · AMIAD, Pˆole Recherche, Palaiseau, France +2
Vision-Language Memory for Spatial Reasoning
Nov 25, 2025Zuntao Liu, Yi Du, Taimeng Fu +3Memory-Augmented VLMs Vision-Language Models
Spatial AI & Robotics (SAIR) Lab, University at Buffalo, USA · Stanford University, USA
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
GraphPilot: Grounded Scene Graph Conditioning for Language-Based Autonomous Driving
Nov 14, 2025Fabian Schmidt, Markus Enzweiler, Abhinav ValadaVision-Language Models VLMs for Autonomous Driving
Esslingen University of Applied Sciences · University of Freiburg
Benchmark Designers Should "Train on the Test Set" to Expose Exploitable Non-Visual Shortcuts
Nov 6, 2025Ellis Brown, Jihan Yang, Shusheng Yang +2Benchmark Auditing VLM Evaluation
New York University
TokenSwap: Backdoor Attack on the Compositional Understanding of Large Vision-Language Models
Sep 29, 2025Zhifang Zhang, Qiqi Tao, Jiaqi Lv +3VLM Robustness Adversarial Attacks on VLMs
School of Computer Science and Engineering, Southeast University, China · School of Electrical Engineering and Computer Science, University of Queensland, Australia · Information Systems Technology and Design Pillar, Singapore University of Technology and Design, Singapore +2
HERMAN: Hierarchical Representation Matching for CLIP-based Class-Incremental Learning
Sep 26, 2025Zhen-Hao Xie, Yan Wang, Lan Li +3Class-Incremental Learning Vision-Language Models
School of Artificial Intelligence and the State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
Image Recognition with Vision and Language Embeddings of VLMs
Sep 11, 2025Illia Volkov, Nikita Kisel, Klara Janouskova +1Image Classification VLM Evaluation
Visual Recognition Group, Faculty of Electrical Engineering, Czech Technical University in Prague Prague, Czechia
TAR: Temporal Anchor-Constrained Reasoning for Video Temporal Grounding
Aug 11, 2025Chaohong Guo, Xun Mo, Yongwei Nie +3Temporal Video Grounding Vision-Language Models
South China University of Technology · Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) · Bytedance Inc.
SalQ-VLM: Fine-Grained Saliency-Guided Quantization for Vision-Language Models
Aug 5, 2025Yufei Xue, Yushi Huang, Lunjie Zhu +2Vision-Language Models VLM Quantization
Institute of Artificial Intelligence (TeleAI), China Telecom · The Hong Kong University of Science and Technology

Mar 30, 2026, cs.AIJ/K move · Enter open · S save

BUZZY: Contrastive Scoring to Mitigate Text-Induced Bias in Multimodal Multiple-Choice QA

Taeyun Roh, Suhyeong Park, Dongyoung Lee, Eunyeong Jo+3

Korea University · AIGEN Sciences · KAIST+1

Vision-Language Models Multimodal QA Multimodal Reasoning Contrastive Decoding Multimodal Understanding

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Multimodal multiple-choice question answering (MCQA) provides a standardized and objectively measurable setting for evaluating vision-language models (VLMs). However, because the MCQA format incorporates the candidate choices into the input context, it introduces several unintended biases. Previous work has primarily focused on structural biases, such as preferences for certain choices. Instead, we argue that the choices act as textual priors, causing models to favor linguistically plausible options regardless of the visual content. We hypothesize and empirically verify that a model genuinely relies on visual evidence only when its multimodal distribution significantly diverges from its text-only distribution. Based on this observation we propose BUZZY,a training-free decoding method that corrects multimodal predictions by subtracting the text-only distribution. Experiments with five VLMs on five multimodal MCQA benchmarks demonstrate that BUZZY achieves the highest average accuracy among state-of-the-art methods while reducing inference latency by over 28% compared to prior contrastive decoding approaches. Overall, these results suggest that amplifying the visual signal by penalizing text-only preferences is key to efficient and robust multimodal MCQA reasoning.

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Latest papers 491