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Deep Learning has driven recent and exciting progress in computer vision,instilling the belief that these algorithms could solve any visual task. Yet,datasets commonly used to train and test computer vision algorithms havepervasive confounding factors. Such biases make it difficult to truly estimatethe performance of those algorithms and how well computer vision models canextrapolate outside the distribution in which they were trained. In this work,we propose a new action classification challenge that is performed well byhumans, but poorly by state-of-the-art Deep Learning models. As aproof-of-principle, we consider three exemplary tasks: drinking, reading, andsitting. The best accuracies reached using state-of-the-art computer visionmodels were 61.7%, 62.8%, and 76.8%, respectively, while human participantsscored above 90% accuracy on the three tasks. We propose a rigorous method toreduce confounds when creating datasets, and when comparing human versuscomputer vision performance. Source code and datasets are publicly available.

Can Deep Learning Recognize Subtle Human Activities?