VLM Adaptation

VLM: Vision-Language Model

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34 papers in the last four weeks, up 209% on the four weeks before. 0.3% of all new papers.

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CharTide: Data-Centric Chart-to-Code Generation via Tri-Perspective Tuning and Inquiry-Driven Evolution
Apr 24, 2026Xiangxi Zheng, Kuang He, Jiayi Hu +6RL for Code Generation VLM Adaptation
Nanjing University · LLM Team, Shopee Pte. Ltd. · East China Normal University +2
Ramen: Robust Test-Time Adaptation of Vision-Language Models with Active Sample Selection
Apr 23, 2026Wenxuan Bao, Yanjun Zhao, Xiyuan Yang +1VLM Robustness Test-Time Adaptation
University of Illinois Urbana-Champaign
Prototype-Based Test-Time Adaptation of Vision-Language Models
Apr 23, 2026Zhaohong Huang, Yuxin Zhang, Wenjing Liu +2Test-Time Adaptation VLM Adaptation
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, 361005, P.R. China.
Reasoning About Traversability: Language-Guided Off-Road 3D Trajectory Planning
Apr 23, 2026Byounggun Park, Soonmin HwangVLM Adaptation Motion Planning
Department of Automotive Engineering, Hanyang University, Seoul, Republic of Korea
InVitroVision: a Multi-Modal AI Model for Automated Description of Embryo Development using Natural Language
Apr 22, 2026Nicklas Neu, Thomas Ebner, Jasmin Primus +4VLM Adaptation Medical VLMs
Software Competence Center Hagenberg GmbH,Softwarepark 32a,Hagenberg, 4232, Upper Austria, Austria · Kinderwunsch Zentrum Kepler Universitätsklinikum,Krankenhausstraße 26, Linz, 4021 Upper Austria, Austria · Wunschkind Klinik Dr. Brunbauer, Ebendorferstraße 6/4 Vienna, 1010, Vienna, Austria
Mitigating Hallucinations in Large Vision-Language Models without Performance Degradation
Apr 22, 2026Xingyu Zhu, Junfeng Fang, Shuo Wang +4Vision-Language Models VLM Adaptation
MoE Key Lab of BIPC, University of Science and Technology of China · National University of Singapore
EmbodiedMidtrain: Bridging the Gap between Vision-Language Models and Vision-Language-Action Models via Mid-training
Apr 21, 2026Yiyang Du, Zhanqiu Guo, Xin Ye +2VLM Adaptation Vision-Language-Action Models
Language Technologies Institute, Carnegie Mellon University · Bosch Research North America & Bosch Center for Artificial Intelligence (BCAI)
Hierarchically Robust Zero-shot Vision-language Models
Apr 20, 2026Junhao Dong, Yifei Zhang, Hao Zhu +2Hierarchical Classification VLM Robustness
1Nanyang Technological University · 2CFAR, IHPC, A*STAR · 3Northwest Polytechnical University +2
T-REN: Learning Text-Aligned Region Tokens Improves Dense Vision-Language Alignment and Scalability
Apr 20, 2026Savya Khosla, Sethuraman T, Aryan Chadha +2VLM Adaptation Open-Vocabulary Semantic Segmentation
University of Illinois Urbana-Champaign
ProtoCLIP: Prototype-Aligned Latent Refinement for Robust Zero-Shot Chest X-Ray Classification
Apr 20, 2026Florian Kittler, Sheethal Bhat, Andreas MaierVLM Robustness Chest X-Ray Classification
Friedrich-Alexander University Erlangen-Nuremberg, Erlangen 91054, Germany
Enhancing Continual Learning of Vision-Language Models via Dynamic Prefix Weighting
Apr 20, 2026Hyeonseo Jang, Hyuk Kwon, Kibok LeeClass-Incremental Learning Vision-Language Models
Yonsei University
GR4CIL: Gap-compensated Routing for CLIP-based Class Incremental Learning
Apr 20, 2026Tianqi Wang, Jingcai GuoClass-Incremental Learning VLM Adaptation
Department of COMP The Hong Kong Polytechnic University Hong Kong SAR · Department of COMP/LSGI The Hong Kong Polytechnic University Hong Kong SAR
Source-Free Domain Adaptation with Vision-Language Prior
Apr 20, 2026Song Tang, Yunxiang Bai, Wenxin Su +3Source-Free Domain Adaptation Cross-Modal Knowledge Distillation
Institute of Machine Intelligence (IMI), University of Shanghai for Science and Technology, Shanghai, China · Technical Aspects of Multimodal Systems (TAMS) Group, Department of Informatics, Universität Hamburg, Hamburg, Germany · European Molecular Biology Laboratory, Heidelberg, Germany +2
BioVLM: Routing Prompts, Not Parameters, for Cross-Modality Generalization in Biomedical VLMs
Apr 19, 2026Mainak Singha, Tanisha Gupta, Ankit Jha +3Cross-Modal Learning Prompt Learning
University of Trento, Italy · Carnegie Mellon University, USA · LNMIIT Jaipur, India +3
Dual-Anchoring: Addressing State Drift in Vision-Language Navigation
Apr 19, 2026Kangyi Wu, Pengna Li, Kailin Lyu +5VLM Adaptation Vision-Language Navigation
National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University · Institute of Automation, Chinese Academy of Sciences, School of Artificial Intelligence, University of Chinese Academy of Sciences · Johns Hopkins University +2
Enhancing Zero-shot Personalized Image Aesthetics Assessment with Profile-aware Multimodal LLM
Apr 19, 2026Chun Wang, Chenfeng Wei, Chenyang Liu +1Zero-Shot Learning Personalized Image Aesthetics Assessment
Mashang Consumer Finance Co., Ltd., Chongqing, China · Xi’an Jiaotong-Liverpool University, Suzhou, China · Beijing University of Posts and Telecommunications, Beijing, China
Cross-Modal Attention Analysis and Optimization in Vision-Language Models: A Study on Visual Reliability
Apr 19, 2026Lijie ZhouCross-Modal Learning VLM Robustness
School of Computer Science University of Nottingham Ningbo China Ningbo, China
MNAFT: modality neuron-aware fine-tuning of multimodal large language models for image translation
Apr 18, 2026Bo Li, Ningyuan Deng, Tianyu Dong +3Fine-Tuning VLM Adaptation
School of Software, Tsinghua University, Beijing, China · Baidu Inc., Beijing, China · School of Information Resource Management, Renmin University of China,Beijing, China +2
Do Vision-Language Models Truly Perform Vision Reasoning? A Rigorous Study of the Modality Gap
Apr 17, 2026Yige Xu, Yongjie Wang, Zizhuo Wu +3Vision-Language Models VLM Adaptation
College of Computing and Data Science, Nanyang Technological University, Singapore · Alibaba-NTU Global e-Sustainability CorpLab (ANGEL) · Tongyi Lab, Alibaba Group, China
AEGIS: Anchor-Enforced Gradient Isolation for Knowledge-Preserving Vision-Language-Action Fine-Tuning
Apr 17, 2026Guransh SinghVLM Adaptation Robotic Manipulation
Independent Researcher
S-GRPO: Unified Post-Training for Large Vision-Language Models
Apr 17, 2026Yuming Yan, Kai Tang, Sihong Chen +4VLM Adaptation Group Relative Policy Optimization
Tencent Shenzhen, Guangdong, China
TTL: Test-time Textual Learning for OOD Detection with Pretrained Vision-Language Models
Apr 17, 2026Jinlun Ye, Jiang Liao, Runhe Lai +4Prompt Learning Test-Time Adaptation
Sun Yat-sen University · Peng Cheng Laboratory · Key Laboratory of Machine Intelligence and Advanced Computing, MOE +2
P3T: Prototypical Point-level Prompt Tuning with Enhanced Generalization for 3D Vision-Language Models
Apr 17, 2026Geunyoung Jung, Soohong Kim, Kyungwoo Song +1Point Cloud Classification VLM Adaptation
Department of Artificial Intelligence, University of Seoul, South Korea · Department of Applied Statistics, Yonsei University, South Korea
AIM: Asymmetric Information Masking for Visual Question Answering Continual Learning
Apr 16, 2026Peifeng Zhang, Zice Qiu, Donghua Yu +4Visual Question Answering Vision-Language Models
Sun Yat-Sen University · National Supercomputing Center in Shenzhen · Tsinghua University
BARD: Bridging AutoRegressive and Diffusion Vision-Language Models Via Highly Efficient Progressive Block Merging and Stage-Wise Distillation
Apr 15, 2026Baoyou Chen, Hanchen Xia, Peng Tu +5Vision-Language Models VLM Adaptation
Shanghai Academy of AI for Science · Fudan University · Shanghai Innovation Institute +1
Dual-Modality Anchor-Guided Filtering for Test-time Prompt Tuning
Apr 14, 2026Jungwon Choi, Eunwoo KimVLM Robustness VLM Adaptation
School of Computer Science and Engineering, Chung-Ang University
A Progressive Training Strategy for Embodied Vision-Language Models to Mitigate Spatio-Temporal Hallucinations
Apr 12, 2026Xiaoda Yang, Shuai Yang, Can Wang +9VLM Adaptation VLM Hallucination
The Hong Kong University of Science and Technology · The Chinese University of Hong Kong
VisionFoundry: Teaching VLMs Visual Perception with Synthetic Images
Apr 10, 2026Guanyu Zhou, Yida Yin, Wenhao Chai +3Visual Question Answering Synthetic Data Augmentation
Princeton University · New York University
Holistic Optimal Label Selection for Robust Prompt Learning under Partial Labels
Apr 8, 2026Yaqi Zhao, Haoliang Sun, Yating Wang +2Prompt Learning VLM Adaptation
School of Software, Shandong University, Jinan, China
CoLA: Cross-Modal Low-rank Adaptation for Multimodal Downstream Tasks
Apr 1, 2026Wish Suharitdamrong, Tony Alex, Muhammad Awais +1Cross-Modal Learning VLM Adaptation
Surrey Institute for People-Centred AI, University of Surrey, Guildford, GU2 7XH, UK · Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey, UK

Apr 24, 2026, cs.CVJ/K move · Enter open · S save

CharTide: Data-Centric Chart-to-Code Generation via Tri-Perspective Tuning and Inquiry-Driven Evolution

Xiangxi Zheng, Kuang He, Jiayi Hu, Ping Yu+5

Nanjing University · LLM Team, Shopee Pte. Ltd. · East China Normal University+2

RL for Code Generation VLM Adaptation Code Generation

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Chart-to-code generation demands strict visual precision and syntactic correctness from Vision-Language Models (VLMs). However, existing approaches are fundamentally constrained by data-centric limitations: despite the availability of growing chart-to-code datasets, simply scaling homogeneous chart-code pairs conflates visual perception with program logic, preventing models from fully leveraging the richness of multimodal supervision. We present CharTide, a novel data-centric framework that systematically redesigns both training and alignment data for chart-to-code generation. First, we construct a 2M-sample dataset via a Tri-Perspective Tuning strategy, explicitly decoupling training into visual perception, pure-text code logic, and modality fusion streams, enabling a 7B model to surpass specialized baselines using only supervised data. Second, we reformulate alignment as a data verification problem rather than a heuristic scoring task. To this end, we introduce an Inquiry-Driven RL framework grounded in the principle of information invariance: a downstream model should yield consistent answers to identical visual queries across both original and generated charts. Moving beyond rigid rule matching or VLM scoring, we employ a frozen Inspector to objectively verify generated charts through atomic QA tasks, providing verifiable reward signals based on answer accuracy. Experiments on ChartMimic, Plot2Code, and ChartX show that CharTide-7B/8B significantly outperforms open-source baselines, surpasses GPT-4o, and is competitive with GPT-5.

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