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.
@article{arxiv.2110.03562,
title = {Weakly Supervised Human-Object Interaction Detection in Video via Contrastive Spatiotemporal Regions},
author = {Shuang Li and Yilun Du and Antonio Torralba and Josef Sivic and Bryan Russell},
journal= {arXiv preprint arXiv:2110.03562},
year = {2021}
}