Introduces VidPair-Halluc benchmark of 1K background-controlled adversarial video pairs and 11K QA pairs generated via PairFlow pipeline to evaluate hallucination in LVMs.
Vistadpo: Video hierarchical spatial-temporal direct preference optimization for large video models.arXiv preprint arXiv:2504.13122 (2025)
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SignDPO uses hierarchical perturbations, self-guided attention-based sampling, and an automated language-level preference generator to align skeleton trajectories with linguistic semantics, outperforming prior gloss-free methods on CSL-Daily, How2Sign, and OpenASL.
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 and 18.58% accuracy gain on ActivityNet-QA.
This is a survey that frames video MLLM research via a human-view formulation of perceptual representations, memory states, reasoning traces, and predictions, then reviews methods, datasets, benchmarks, and open problems.
citing papers explorer
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No Place to Hide: Benchmarking Video Hallucination with Background-Controlled Pairs
Introduces VidPair-Halluc benchmark of 1K background-controlled adversarial video pairs and 11K QA pairs generated via PairFlow pipeline to evaluate hallucination in LVMs.
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SignDPO: Multi-level Direct Preference Optimisation for Skeleton-based Gloss-free Sign Language Translation
SignDPO uses hierarchical perturbations, self-guided attention-based sampling, and an automated language-level preference generator to align skeleton trajectories with linguistic semantics, outperforming prior gloss-free methods on CSL-Daily, How2Sign, and OpenASL.
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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 and 18.58% accuracy gain on ActivityNet-QA.
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Watch, Remember, Reason: Human-View Video Understanding with MLLMs
This is a survey that frames video MLLM research via a human-view formulation of perceptual representations, memory states, reasoning traces, and predictions, then reviews methods, datasets, benchmarks, and open problems.