Large Vision-Language Models

Also known as LVLM

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30 papers in the last four weeks, up 36% on the four weeks before. 0.3% of all new papers.

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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
Multi-modal Reasoning with LLMs for Visual Semantic Arithmetic
Apr 21, 2026Chuou Xu, Liya Ji, Qifeng ChenLarge Vision-Language Models VLM Reasoning
The Hong Kong University of Science and Technology
VCE: A zero-cost hallucination mitigation method of LVLMs via visual contrastive editing
Apr 21, 2026Yanbin Huang, Yisen Li, Guiyao Tie +6Large Vision-Language Models Object Hallucination in VLMs
1Huazhong University of Science and Technology · Institute of Artificial Intelligence (TeleAI), China Telecom
ST-Prune: Training-Free Spatio-Temporal Token Pruning for Vision-Language Models in Autonomous Driving
Apr 21, 2026Lin Sha, Haiyun Guo, Tao Wang +4VLMs for Autonomous Driving Efficient VLM Inference
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China · Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China · Carizon, Beijing 100094, China
DUALVISION: RGB-Infrared Multimodal Large Language Models for Robust Visual Reasoning
Apr 20, 2026Abrar Majeedi, Zhiyuan Ruan, Ziyi Zhao +3Cross-Modal Learning Visual Question Answering
University of Wisconsin-Madison · Amazon · Northeastern University
SIF: Semantically In-Distribution Fingerprints for Large Vision-Language Models
Apr 18, 2026Yifei Zhao, Qian Lou, Mengxin ZhengVLM Robustness Language Model Fingerprinting
University of Central Florida
Where Do Vision-Language Models Fail? World Scale Analysis for Image Geolocalization
Apr 17, 2026Siddhant Bharadwaj, Ashish Vashist, Fahimul Aleem +1VLM Evaluation Large Vision-Language Models
University of Central Florida
HyperGVL: Benchmarking and Improving Large Vision-Language Models in Hypergraph Understanding and Reasoning
Apr 17, 2026Yanbin Wei, Chun Kang, Siwei Li +11VLM Evaluation Large Vision-Language Models
1Southern University of Science and Technology · 2Hong Kong University of Science and Technology · 4Beihang University +1
How Do LLMs and VLMs Understand Viewpoint Rotation Without Vision? An Interpretability Study
Apr 16, 2026Zhen Yang, Ping Jian, Zhongbin Guo +5Visual Spatial Reasoning Large Vision-Language Models
School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China · Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
IWP: Token Pruning as Implicit Weight Pruning in Large Vision Language Models
Apr 1, 2026Dong-Jae Lee, Sunghyun Baek, Junmo KimEfficient VLM Inference Large Vision-Language Models
KAIST, South Korea
When Sinks Help or Hurt: Unified Framework for Attention Sink in Large Vision-Language Models
Apr 1, 2026Jiho Choi, Jaemin Kim, Sanghwan Kim +2Large Vision-Language Models VLM Interpretability
KAIST, South Korea · Chung-Ang University, South Korea · Technical University of Munich, Germany
GeoHeight-Bench: Towards Height-Aware Multimodal Reasoning in Remote Sensing
Mar 26, 2026Xuran Hu, Zhitong Xiong, Zhongcheng Hong +3Remote Sensing Image Understanding Large Vision-Language Models
KTH Royal Institute of Technology · Technical University of Munich · The Hong Kong University of Science and Technology (Guangzhou)
Revealing Multi-View Hallucination in Large Vision-Language Models
Mar 25, 2026Wooje Park, Insu Lee, Soohyun Kim +4Visual Question Answering Hallucination in Language Models
Seoul National University · Sungkyunkwan University
DetPO: In-Context Learning with Multi-Modal LLMs for Few-Shot Object Detection
Mar 24, 2026Gautam Rajendrakumar Gare, Neehar Peri, Matvei Popov +3Large Vision-Language Models Few-Shot Object Detection
Carnegie Mellon University · Roboflow
Neural Gate: Mitigating Privacy Risks in LVLMs via Neuron-Level Gradient Gating
Mar 13, 2026Xiangkui Cao, Jie Zhang, Meina Kan +2Large Vision-Language Models Privacy Protection in VLMs
State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences University of Chinese Academy of Sciences · University of Chinese Academy of Sciences
Cultural Counterfactuals: Evaluating Cultural Biases in Large Vision-Language Models with Counterfactual Examples
Mar 2, 2026Phillip Howard, Xin Su, Kathleen C. FraserVLM Evaluation Counterfactual Evaluation of VLMs
Thoughtworks · University of Ottawa
AGMark: Attention-Guided Dynamic Watermarking for Large Vision-Language Models
Feb 10, 2026Yue Li, Xin Yi, Dongsheng Shi +3Large Vision-Language Models Robust Watermarking
East China Normal University Shanghai, China · Hasso Plattner Institute Potsdam, Germany · University of Potsdam Potsdam, Germany +1
SPARC: Separating Perception And Reasoning Circuits for Test-time Scaling of VLMs
Feb 6, 2026Niccolo Avogaro, Nayanika Debnath, Li Mi +6Visual Question Answering Efficient VLM Inference
ETH Zürich · IBM Research · MIT-IBM Watson AI Lab.
Stage-wise Attention-Guided Region Sequencing for Adversarial Attacks on Large Vision-Language Models
Feb 4, 2026Jaehyun Kwak, Nam Cao, Boryeong Cho +3Adversarial Attacks on VLMs Adversarial Attacks
KAIST · KENTECH
A Comparative Evaluation of Large Vision-Language Models for 2D Object Detection under SOTIF Conditions
Jan 30, 2026Ji Zhou, Yilin Ding, Yongqi Zhao +4VLM Evaluation Large Vision-Language Models
Institute of Automotive Engineering, Graz University of Technology, 8010 Graz, Austria · Independent Researcher, 200000 Shanghai, China · School of Intelligent Connected Vehicle, Hubei University of Automotive Technology, 442002 Shiyan, China +1
R3G: A Reasoning-Retrieval-Reranking Framework for Vision-Centric Answer Generation
Jan 25, 2026Zhuohong Chen, Zhengxian Wu, Zirui Liao +6Multimodal RAG Visual Question Answering
1The Shenzhen International Graduate School, Tsinghua University, China · School of Computer Science and Information Engineering, Hefei University of Technology, China · 2State Key Laboratory of Nuclear Power Safety Technology and Equipment, China
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
From Words to Wavelengths: VLMs for Few-Shot Multispectral Object Detection
Dec 17, 2025Manuel Nkegoum, Minh-Tan Pham, Élisa Fromont +2VLM Adaptation Multispectral Object Detection
Univ Bretagne Sud, IRISA, UMR 6074, Vannes, France · ATERMES, Montigny-le-Bretonneux, France · Univ Rennes, IRISA, UMR 6074, Rennes, France +1
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
Efficient-VLN: A Simple yet Strong Baseline for Efficient Vision-Language Navigation
Dec 11, 2025Duo Zheng, Shijia Huang, Yanyang Li +1KV Caching Efficient VLM Inference
The Chinese University of Hong Kong · Weitu AI
Visual Species Recognition with Large Multimodal Models as Post-Hoc Correctors
Dec 10, 2025Tian Liu, Anwesha Basu, James Caverlee +1Large Vision-Language Models Multimodal Large Language Models
Department of Computer Science and Engineering, Texas A&M University · Department of AI, University of Macau
From Hindsight to Foresight: Self-Encouraged Hindsight Distillation for Knowledge-based Visual Question Answering
Nov 14, 2025Yu Zhao, Ying Zhang, Xuhui Sui +4Visual Question Answering CoT Distillation
College of Computer Science, VCIP, DISSec, Nankai University, Tianjin, China · School of Software, Tiangong University, Tianjin, China · Shenzhen Campus of Sun Yat-sen University, Shenzhen, China +1
GUI-AIMA: Aligning Intrinsic Multimodal Attention with a Context Anchor for GUI Grounding
Nov 2, 2025Shijie Zhou, Viet Dac Lai, Hao Tan +4Multimodal Grounding GUI Grounding
University at Buffalo, USA · Adobe Research, USA
Activation-Informed Pareto-Guided Low-Rank Compression for Efficient LLM/VLM
Oct 7, 2025Ryan Solgi, Parsa Madinei, Jiayi Tian +4Model Compression LLM Compression
University of California-Santa Barbara, USA · Amazon, USA
VISOR++: Universal Visual Inputs based Steering for Large Vision Language Models
Sep 29, 2025Ravikumar Balakrishnan, Mansi PhuteLanguage Model Steering Large Vision-Language Models
HiddenLayer, USA · Georgia Institute of Technology, USA

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Mitigating Hallucinations in Large Vision-Language Models without Performance Degradation

Xingyu Zhu, Junfeng Fang, Shuo Wang, Beier Zhu+3

MoE Key Lab of BIPC, University of Science and Technology of China · National University of Singapore

Vision-Language Models VLM Adaptation Hallucination in Language Models VLM Hallucination Large Vision-Language Models Representation Disentanglement+1

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Large Vision-Language Models (LVLMs) exhibit powerful generative capabilities but frequently produce hallucinations that compromise output reliability. Fine-tuning on annotated data devoid of hallucinations offers the most direct solution, while its high computational cost motivates recent representation-based methods, which focus on mitigating hallucinatory components within hidden representations. Though efficient, we empirically observe that these methods degrade general generation capacity due to incomplete extraction of hallucination components and non-selective parameter updates. To address these limitations, we propose MPD, a dual-stage framework for mitigating hallucinations without performance degradation. Specifically, our MPD relies on two essential factors: (1) semantic-aware component disentanglement to extract pure hallucination components, and (2) interpretable parameter updates that selectively modify parameters most relevant to hallucination. Extensive experiments demonstrate that MPD achieves state-of-the-art performance, reducing hallucinations by 23.4% while maintaining 97.4% of general generative capability as evaluated on LLaVA-Bench and MME, with no additional computational cost.

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Latest papers 253