Multi-Task Learning of Object State Changes from Uncurated Videos

• URL: http://arxiv.org/abs/2211.13500v1
• Date: Thu, 24 Nov 2022 09:42:46 GMT
• Title: Multi-Task Learning of Object State Changes from Uncurated Videos
• Authors: Tom\'a\v{s} Sou\v{c}ek and Jean-Baptiste Alayrac and Antoine Miech and Ivan Laptev and Josef Sivic
• Abstract summary: We learn to temporally localize object state changes by observing people interacting with objects in long uncurated web videos. We show that our multi-task model achieves a relative improvement of 40% over the prior single-task methods. We also test our method on long egocentric videos of the EPIC-KITCHENS and the Ego4D datasets in a zero-shot setup.
• Score: 55.60442251060871
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We aim to learn to temporally localize object state changes and the corresponding state-modifying actions by observing people interacting with objects in long uncurated web videos. We introduce three principal contributions. First, we explore alternative multi-task network architectures and identify a model that enables efficient joint learning of multiple object states and actions such as pouring water and pouring coffee. Second, we design a multi-task self-supervised learning procedure that exploits different types of constraints between objects and state-modifying actions enabling end-to-end training of a model for temporal localization of object states and actions in videos from only noisy video-level supervision. Third, we report results on the large-scale ChangeIt and COIN datasets containing tens of thousands of long (un)curated web videos depicting various interactions such as hole drilling, cream whisking, or paper plane folding. We show that our multi-task model achieves a relative improvement of 40% over the prior single-task methods and significantly outperforms both image-based and video-based zero-shot models for this problem. We also test our method on long egocentric videos of the EPIC-KITCHENS and the Ego4D datasets in a zero-shot setup demonstrating the robustness of our learned model.

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