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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Attributes Should Come from Images, Not Class Names: Distribution-Conditioned Attribute Selection for Vision-Language Models
Jul 21, 2026Gautam Rajendrakumar Gare, Jia Shi, Zhiqiu Lin +3Vision-Language Models Distribution Shift
Carnegie Mellon University, USA
Stochastic Meta-Unlearning: Bridging Language Backbone and Multimodal Unlearning
Jul 21, 2026Zijie Liu, Jinhao Duan, Gaowen Liu +2VLM Unlearning Vision-Language Models
UNC at Chapel Hill · Cisco Research · Michigan State University
Simple Domain Generalization for Strong Pixel-Level Image Tampering Detection in Modern VLMs
Jul 20, 2026Yi Tang, Xinyi Shang, Jiacheng Cui +12Vision-Language Models Image Forgery Detection
Mohamed bin Zayed University of Artificial Intelligence · University College London · Weizmann Institute of Science
StepX-Edge: An On-Device UI Vision-Language Model via Architecture-Training-Deployment Co-Design
Jul 20, 2026Yin Wang, Haotian Hu, Jineng Han +4Vision-Language Models VLM Quantization
StepX Team
FUSAR-R1: A Large-Scale Reasoning Model for Intelligent Interpretation of SAR Images
Jul 18, 2026Yi Yang, Xiaokun Zhang, Yuxuan Li +3Remote Sensing Image Understanding Vision-Language Models
Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai 200433, China
Region-Grounded Vision-Language Learning for Detection-Guided Mammographic Lesion Classification
Jul 17, 2026Zhengbo Zhou, Jiren Li, Dooman Arefan +2Radiology VLMs Vision-Language Models
Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA · Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA · Department of Biomedical Informatics and Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
Ask Twice, Look Twice: Prompt Echoing Resolves the Question-First Paradox in Vision-Language Models
Jul 17, 2026Rakshanda Hassan Abhinandan, John Galeotti, Deva Ramanan +1Visual Question Answering Vision-Language Models
Carnegie Mellon University, USA
GeoDetect: Geometric Adversarial Detection for VLPs
Jul 16, 2026Afsaneh Hasanebrahimi, Hanxun Huang, Christopher Leckie +2VLM Robustness Adversarial Attacks on VLMs
The University of Melbourne, Melbourne, Australia · Monash University, Melbourne, Australia
SportD: How do VLMs physically strategize?
Jul 16, 2026Jasin Cekinmez, Addison J. Wu, Haotian Xia +6VLM Evaluation Vision-Language Models
Princeton University · Rice University · UC Irvine +2
SoftNav: Injecting 3D Scene Tokens into VLMs for Embodied Navigation
Jul 16, 2026Yi Wu, Junjie An, Xiao Liu +6Vision-Language Models Embodied Navigation
Zhejiang University · Shandong University
Attention-Free and Lightweight Token Reduction for Efficient Vision-Language Models
Jul 15, 2026Xuanyi Hao, Zuoyuan Zhang, Zhibo Wang +4Vision-Language Models Efficient VLM Inference
State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou 310027, China · Hong Kong University of Science and Technology, Hong Kong SAR, China · School of Artificial Intelligence, Nanchang University, Nanchang 330038, China
What Keeps Vision-Language Models Looking at the Image?
Jul 14, 2026Hiroto Osaka, Shohei Taniguchi, Gouki Minegishi +3Vision-Language Models VLM Interpretability
The University of Tokyo
MaxSAT-Based Feedback for Guiding Vision-Language Models in Sudoku
Jul 14, 2026Pedro Orvalho, Guillem Alenyà, Felip ManyàVision-Language Models VLM Reasoning
Artificial Intelligence Research Institute (IIIA), Consejo Superior de Investigaciones Científicas (CSIC), Barcelona, Catalonia, Spain · Institut de Robòtica i Informàtica Industrial (IRI-CSIC-UPC), Barcelona, Spain
The Emerging Paradigm of Geospatial Foundation Models: From Pre-Training to Agentic Reasoning
Jul 13, 2026Shelley CazaresVision-Language Models Geospatial Foundation Models
Google Public Sector, Washington DC 20001, USA
When Depth Is Better Told Than Shown: Depth-Ordinal Prompting for Vision-Language Spatial Reasoning
Jul 13, 2026Quynh Vo, Phuc Dao, Cong-Duy Nguyen +1Vision-Language Models Visual Spatial Reasoning
National University of Singapore · Center of AI Research, VinUniversity
SVR-R1: Bootstrapping Multi-modal Reasoning with Self-verification in Reinforcement Learning
Jul 13, 2026Mingyuan Wu, Jingcheng Yang, Shengyi Qian +11Vision-Language Models Multimodal Reasoning
University of Illinois Urbana-Champaign · Meta
Mixture of Cognitive Experts in Large Vision-Language Models
Jul 12, 2026Robert Wijaya, Ngai-Man CheungVision-Language Models Large Vision-Language Models
Singapore University of Technology and Design (SUTD), Singapore
SynthDocBench: Controlled Benchmark for Long-Context Visual Document Understanding
Jul 11, 2026Abhigya Verma, Khyati Mahajan, Amit Kumar Saha +4VLM Evaluation Vision-Language Models
ServiceNow AI · Mila · Université de Montréal
WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding
Jul 11, 2026Xianzhi Ma, Shujun Wang, Xiaohan Li +3Remote Sensing Image Understanding Vision-Language Models
School of Frontier Sciences, Nanjing University, Suzhou, China · Computer Network Information Center, Chinese Academy of Sciences, Beijing, China
The Count Is There, but Misaligned: Understanding and Correcting Counting Failures in VLMs
Jul 10, 2026Ahmed Oumar El-Shangiti, Abzal Nurgazy, Hilal AlQuabeh +2Vision-Language Models VLM Interpretability
MBZUAI · Imperial College London
MOSAIC: Adaptive Inter-layer Composition for Efficient Heterogeneous Vision-Language Models
Jul 10, 2026Yuncheng Yang, Feiyang Ye, Shixian Luo +7Vision-Language Models Efficient VLM Inference
LiAuto Inc.
MultiView-Bench: A Diagnostic Benchmark for World-Centric Multi-View Integration in VLMs
Jul 9, 2026Hantao Zhang, Jinru Sui, Ed Li +2VLM Evaluation Vision-Language Models
Yale University · University of Edinburgh
FSD-VLN: Fast-Slow Dual-System Modeling for Aerial Long-Horizon Vision-Language Navigation
Jul 9, 2026Xueke Zhu, Qingyan Meng, Liutao Yu +4Vision-Language Models UAV Navigation
Pengcheng Laboratory · Shenzhen Institutes of Advanced Technology · Peking University
Dive Into the Implicit Biases of Low-rank Vision-language Alignment
Jul 9, 2026Mingjia Shi, Shuo Wang, Xiaobo Wang +7Vision-Language Models Low-Rank Adaptation
Mininglamp · Shenzhen University of Advanced Technology · National University of Singapore
When Thinking Hurts: Epistemic Signals in the Reasoning Chains of Visual Language Models
Jul 9, 2026Mayank SingalVLM Evaluation Uncertainty Estimation for VLA Models
Independent Researcher.
Rethinking Small VLM Quantization: From Component-Wise Analysis to Hardware-Aware Edge Deployment
Jul 9, 2026Hyeju Shin, Chorwon Kim, Ryangsoo Kim +2Mixed-Precision Quantization Vision-Language Models
1ETRI, Republic of Korea.
When Prompts Ignore Structure: Graph-Based Attribute Reasoning for Calibrated VLMs
Jul 8, 2026Tanay Sodha, Aditya Sharma, Ramya Hebbalaguppe +2Vision-Language Models VLM Adaptation
Department of Computer Science and Information Systems, Birla Institute of Technology and Science, Pilani, India · TCS Research, New Delhi, India
BUS: Brain-Inspired Unsupervised Self-Reflection via Backward Prediction for Multimodal Reasoning
Jul 8, 2026Jiacheng Yang, Tongying Xiao, Yunkai Dang +7Vision-Language Models VLM Reasoning
State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing, China · Institute of Brain-inspired Intelligence, Nanjing University, Nanjing, China · Microsoft, Redmond, WA, USA +1
Is the Geometry Doing the Work? An Operating-Point Audit of Hierarchy in Hyperbolic Vision-Language Models
Jul 6, 2026Jaeyoung Kim, Eunseok Kim, Dongsuk JangVLM Evaluation Vision-Language Models
MADI
HunyuanOCR-1.5: Making Lightweight OCR VLMs Faster and Better
Jul 6, 2026Gengluo Li, Xingyu Wan, Shangpin Peng +20Vision-Language Models Efficient VLM Inference
Institute of Information Engineering, Chinese Academy of Sciences · Large Language Model Department, Tencent · Nankai University

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Attributes Should Come from Images, Not Class Names: Distribution-Conditioned Attribute Selection for Vision-Language Models

Gautam Rajendrakumar Gare, Jia Shi, Zhiqiu Lin, Deepak Pathak+2

Carnegie Mellon University, USA

Vision-Language Models Distribution Shift VLM Interpretability Classification Zero-Shot Image Classification Image Classification

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A popular route to interpretable zero-shot classification asks a large language model (LLM) to describe each class name and prompts CLIP with the resulting descriptors. We show that these descriptors carry little visual evidence of their own: removing the class name from the prompt collapses ImageNet accuracy from 59.5% to 15.5%. The diagnosis is that the descriptors are conditioned on the label rather than on the images, so they describe the concept in general and mislead exactly when the data shifts; an LLM insists that strawberries are red, but every strawberry in ImageNet-Sketch is a colorless line drawing. We therefore select attributes from the target image collection instead: we score a large attribute pool against the images in CLIP's joint embedding space and keep the top-scoring attributes per class. Selected this way, class-name-free attribute prompts reach 23.8% on ImageNet (against 15.5% for LLM descriptors), the gain holds on four shifted ImageNet variants, and reselecting from the LLM's own pool isolates the selection mechanism as the cause. With one image per class, the selected attributes outperform the prompt-tuning method CoOp by 3 points while fitting in under a minute instead of 14 hours, with no learned soft prompt to obscure the decision. Because the attribute set is chosen by the data, it doubles as a readable summary of a dataset, which we use to describe distribution shift in words. Our code and results are available on our project page: https://ggare-cmu.github.io/AttributeSelect/

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