Revealing Multi-View Hallucination in Large Vision-Language Models

arXiv:2603.23934 · cs.CV, cs.AI · Submitted 2026-03-25 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-03-25

Updated: 2026-09-01

Terminology

• Hallucination — 71× in this paper · explained in 3 episodes
• Accuracy — 18× in this paper · explained in 1 episode
• Benchmark — 16× in this paper · explained in 2 episodes
• Verification — 10× in this paper · explained in 2 episodes
• Grounding — 7× in this paper · explained in 3 episodes
• Hallucination Mitigation — 7× in this paper · explained in 2 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• Reference Shift Contrastive Decoding — 4× in this paper
• Visual Grounding — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Adam — 3× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• Digital Twin — 2× in this paper · explained in 4 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Conference on Computer Vision and Pattern Recognition — 1× in this paper
• Consistency — 1× in this paper · explained in 3 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes

Sources

• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
• Seeing is Believing: Mitigating Hallucination in Large Vision-Language Models via CLIP-Guided Decoding
• Exploring Hallucination of Large Multimodal Models in Video Understanding: Benchmark, Analysis and Mitigation
• Qwen2.5-VL Technical Report
• DAMRO: Dive into the Attention Mechanism of LVLM to Reduce Object Hallucination
• An Introduction to Vision-Language Modeling
• Matterport3D: Learning from RGB-D Data in Indoor Environments
• VidHal: Benchmarking Temporal Hallucinations in Vision LLMs
• LLaVA-OneVision: Easy Visual Task Transfer
• GPT-4o System Card
• LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models
• Evaluating Object Hallucination in Large Vision-Language Models
• A Survey of State of the Art Large Vision Language Models: Alignment, Benchmark, Evaluations and Challenges
• VACoDe: Visual Augmented Contrastive Decoding
• DeepSeek-VL: Towards Real-World Vision-Language Understanding
• Towards Comprehensive Scene Understanding: Integrating First and Third-Person Views for LVLMs
• Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
• InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
• Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding
• UniDepthV2: Universal Monocular Metric Depth Estimation Made Simpler

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