arXiv Computer Vision
Video2Reaction: Training Foundation Video Models to Predict Audience Reaction
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Video2Reaction: Mapping Video to Audience Reaction Distribution in the Wild
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Why Do Vision Language Models Struggle To Recognize Human Emotions?
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Multimodal Large Language Models as Synthetic Participants in Video-Based Studies: An Evaluation
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HSEmotion Team at the 11th ABAW Challenge: Multi-Task Learning and Ambivalence/Hesitancy Video Recognition
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Hugging Face Trending Papers
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HarmVideoBench: Benchmarking Harmful Video Understanding in Large Multimodal Models
Large vision-language models (LVLMs) have recently shown immense potential in automated content moderation, sparking growing interest in developing harmful-video benchmarks. However, we identify two primary limitations in existing works: 1) The multi-layered characteristics of harmful videos are overlooked.
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Sa2VA: Marrying SAM2 with MLLM for Dense Grounded Understanding of Images and Videos
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