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arXiv · 1911.11206

Oops! Predicting Unintentional Action in Video

Dave Epstein·Boyuan Chen·Carl Vondrick
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Abstract

From just a short glance at a video, we can often tell whether a person's action is intentional or not. Can we train a model to recognize this? We introduce a dataset of in-the-wild videos of unintentional action, as well as a suite of tasks for recognizing, localizing, and anticipating its onset. We train a supervised neural network as a baseline and analyze its performance compared to human consistency on the tasks. We also investigate self-supervised representations that leverage natural signals in our dataset, and show the effectiveness of an approach that uses the intrinsic speed of video to perform competitively with highly-supervised pretraining. However, a significant gap between machine and human performance remains. The project website is available at https://oops.cs.columbia.edu

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Dave Epstein, Boyuan Chen, Carl Vondrick. 2019-11-25. Oops! Predicting Unintentional Action in Video. https://arxiv.org/abs/1911.11206

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