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cs.CV2026
MultiToP: Learning to Patch Visual Tokens to Mitigate Hallucinations in Video Large Multimodal Models
Yuansheng Gao, Wenbin Xing, Jiahao Yuan +4
Video Large Multimodal Models have achieved remarkable progress in video understanding, yet they remain prone to hallucinations, where generated responses are not faithfully suppor…
cs.CV2026
Learning to Decode Against Compositional Hallucination in Video Multimodal Large Language Models
Wenbin Xing, Quanxing Zha, Lizheng Zu +3
Current research on video hallucination mitigation primarily focuses on isolated error types, leaving compositional hallucinations, arising from incorrect reasoning over multiple i…
cs.CV2026
Enhancing Video Representations with Spatiotemporal-Semantic Residual to Mitigate Hallucinations in Video Large Multimodal Models
Yuansheng Gao, Jinman Zhao, Tong Zhang +5
Although Video Large Multimodal Models have achieved strong performance in video understanding, they still suffer from hallucination. Existing inference-time intervention methods u…