Oops! Predicting Unintentional Action in Video
Dave Epstein, Boyuan Chen, Carl Vondrick
2020年份
36顶会引用
摘要
Did this person intend for this action to happen, or was it an accident? In this paper, we introduce a large in-the-wild video dataset of unintentional action. Our dataset, which we have collected by downloading "fail" videos from the web, contains over twenty thousand clips, and they span a diverse number of activities and scenes. Using this dataset, we study a variety of visual clues for learning to predict intentionality in video.
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引用它的顶会 Paper36
- Ego4D: Around the World in 3, 000 Hours of Egocentric VideoKristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis 等CVPR 2022 · 被引用 525 次
- Self-supervised Co-Training for Video Representation LearningTengda Han, Weidi Xie, Andrew ZissermanNeurIPS 2020 · 被引用 405 次
- RSPNet: Relative Speed Perception for Unsupervised Video Representation LearningPeihao Chen, Deng Huang, Dongliang He, Xiang Long 等AAAI 2021 · 被引用 140 次
- Eagle 2.5: Boosting Long-Context Post-Training for Frontier Vision-Language ModelsGuo Chen, Zhiqi Li, Shihao Wang, Jindong Jiang 等NeurIPS 2025 · 被引用 69 次
- Bifold and Semantic Reasoning for Pedestrian Behavior PredictionAmir Rasouli, Mohsen Rohani, Jun LuoICCV 2021 · 被引用 69 次
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