Weakly Supervised Human-Object Interaction Detection in Video via Contrastive Spatiotemporal Regions

• URL: http://arxiv.org/abs/2110.03562v1
• Date: Thu, 7 Oct 2021 15:30:18 GMT
• Title: Weakly Supervised Human-Object Interaction Detection in Video via Contrastive Spatiotemporal Regions
• Authors: Shuang Li, Yilun Du, Antonio Torralba, Josef Sivic, and Bryan Russell
• Abstract summary: A system does not know what human-object interactions are present in a video as or the actual location of the human and object. We introduce a dataset comprising over 6.5k videos with human-object interaction that have been curated from sentence captions. We demonstrate improved performance over weakly supervised baselines adapted to our annotations on our video dataset.
• Score: 81.88294320397826
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce the task of weakly supervised learning for detecting human and object interactions in videos. Our task poses unique challenges as a system does not know what types of human-object interactions are present in a video or the actual spatiotemporal location of the human and the object. To address these challenges, we introduce a contrastive weakly supervised training loss that aims to jointly associate spatiotemporal regions in a video with an action and object vocabulary and encourage temporal continuity of the visual appearance of moving objects as a form of self-supervision. To train our model, we introduce a dataset comprising over 6.5k videos with human-object interaction annotations that have been semi-automatically curated from sentence captions associated with the videos. We demonstrate improved performance over weakly supervised baselines adapted to our task on our video dataset.

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