MoHallBench: A Benchmark for Motion Hallucination in Video Large Language Models
Video Large Language Models (VideoLLMs) have shown strong progress in video understanding, yet they still suffer from hallucinations that are inconsistent with visual evidence. Existing benchmarks mainly focus on object hallucination or coarse action perception, leaving a key video-specific problem underexplored: motion hallucination, in which models infer human motions that are absent from the video. We present MoHallBench, a benchmark for diagnosing motion hallucination in VideoLLMs. MoHallBench systematically evaluates three major sources of hallucination: co-occurrence priors, sequential i
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- LinkedLinked via unknownVioletVision-3B →
- LinkedLinked via arxiv author · 85%Jiale Li →
“MoHallBench: A Benchmark for Motion Hallucination in Video Large Language Models”
- LinkedLinked via arxiv author · 85%Sihan Chen →
“MoHallBench: A Benchmark for Motion Hallucination in Video Large Language Models”
- LinkedLinked via arxiv author · 85%Mengyuan Liu →
“MoHallBench: A Benchmark for Motion Hallucination in Video Large Language Models”
- PossiblePossibly related (embedding) · 47%Models Produce Hallucinations Because of Probabilistic Training - Let's Data Science →
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzySimilar title/name (fuzzy) · 59%Developer-Y/cs-video-courses →
“Fuzzy title match (0.73): “MoHallBench: A Benchmark for Motion Hallucination in Video L” ≈ “Developer-Y/cs-video-courses””
- FuzzySimilar title/name (fuzzy) · 59%jeinlee1991/chinese-llm-benchmark →
“Fuzzy title match (0.73): “MoHallBench: A Benchmark for Motion Hallucination in Video L” ≈ “jeinlee1991/chinese-llm-benchmark””