Video Representations of Goals Emerge from Watching Failure
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We introduce a video representation learning framework that models the latent goals behind observable human action. Motivated by how children learn to reason about goals and intentions by experiencing failure, we leverage unconstrained video of unintentional action to learn without direct supervision. Our approach models videos as contextual trajectories that represent both low-level motion and high-level action features. Experiments and visualizations show the model is able to predict underlying goals, detect when action switches from intentional to unintentional, and automatically correct unintentional action. Although the model is trained with minimal supervision, it is competitive with highly-supervised baselines, underscoring the role of failure examples for learning goal-oriented video representations. The project website is available at https://aha.cs.columbia.edu/
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