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Register a new userOops! Predicting Unintentional Action in Video
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Dave Epstein
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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From just a short glance at a video, we can often tell whether a persons 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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Action recognition is a crucial task for video understanding. In this paper, we present AutoVideo, a Python system for automated video action recognition. It currently supports seven action recognition algorithms and various pre-processing modules. Unlike the existing libraries that only provide model zoos, AutoVideo is built with the standard pipeline language. The basic building block is primitive, which wraps a pre-processing module or an algorithm with some hyperparameters. AutoVideo is highly modular and extendable. It can be easily combined with AutoML searchers. The pipeline language is quite general so that we can easily enrich AutoVideo with algorithms for various other video-related tasks in the future. AutoVideo is released under MIT license at https://github.com/datamllab/autovideo
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