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VideoHallucer: Evaluating Intrinsic and Extrinsic Hallucinations in Large Video-Language Models
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Recent advancements in Multimodal Large Language Models (MLLMs) have extended their capabilities to video understanding. Yet, these models are often plagued by "hallucinations", where irrelevant or nonsensical content is generated, deviating from the actual video context. This work introduces VideoHallucer, the first comprehensive benchmark for hallucination detection in large video-language models (LVLMs). VideoHallucer categorizes hallucinations into two main types: intrinsic and extrinsic, offering further subcategories for detailed analysis, including object-relation, temporal, semantic detail, extrinsic factual, and extrinsic non-factual hallucinations. We adopt an adversarial binary VideoQA method for comprehensive evaluation, where pairs of basic and hallucinated questions are crafted strategically. By evaluating eleven LVLMs on VideoHallucer, we reveal that i) the majority of current models exhibit significant issues with hallucinations; ii) while scaling datasets and parameters improves models' ability to detect basic visual cues and counterfactuals, it provides limited benefit for detecting extrinsic factual hallucinations; iii) existing models are more adept at detecting facts than identifying hallucinations. As a byproduct, these analyses further instruct the development of our self-PEP framework, achieving an average of 5.38% improvement in hallucination resistance across all model architectures.
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Cited by 19 Pith papers
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TOC-Bench: A Temporal Object Consistency Benchmark for Video Large Language Models
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Enhancing Video Representations with Spatiotemporal-Semantic Residual to Mitigate Hallucinations in Video Large Multimodal Models
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MESH -- Understanding Videos Like Human: Measuring Hallucinations in Large Video Models
MESH, a three-layer video hallucination benchmark, shows LVMs ace basic objects and coarse traits but slip badly on fine character details and multi-subject actions in longer clips.
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SportsTime plus Chain-of-Time Reasoning (temporal-reward GRPO and anchor-observe-infer) modestly lifts open-ended sports VideoQA and step-wise temporal grounding over 4B–8B MLLM baselines.
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MultiToP: Learning to Patch Visual Tokens to Mitigate Hallucinations in Video Large Multimodal Models
MultiToP mitigates hallucinations in video multimodal models by training a Visual Token Patcher with information-guided rank calibration to selectively replace unreliable tokens, yielding 50.60% F1 gain on Vript-HAL a...
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