Uncertainty-aware Weakly Supervised Action Detection from Untrimmed Videos
Abstract
Despite the recent advances in video classification, progress in spatio-temporal action recognition has lagged behind.
A major contributing factor has been the prohibitive cost of annotating videos frame-by-frame.
In this paper, we present a spatio-temporal action recognition that is trained with only video-level labels, which are significantly easier to annotate, and can even be mined automatically (subject to some label noise).
Our method leverages per-frame person detectors which have been trained on large image datasets within a Multiple Instance Learning framework.
We show how we can apply our method in cases where the standard Multiple Instance Learning assumption, that each bag contains at least one instance with the specified label, is invalid using a novel probabilistic variant of MIL.
Furthermore, we report the first weakly-supervised results on the AVA dataset and state-of-the-art results among weakly-supervised methods on UCF101-24.
A major contributing factor has been the prohibitive cost of annotating videos frame-by-frame.
In this paper, we present a spatio-temporal action recognition that is trained with only video-level labels, which are significantly easier to annotate, and can even be mined automatically (subject to some label noise).
Our method leverages per-frame person detectors which have been trained on large image datasets within a Multiple Instance Learning framework.
We show how we can apply our method in cases where the standard Multiple Instance Learning assumption, that each bag contains at least one instance with the specified label, is invalid using a novel probabilistic variant of MIL.
Furthermore, we report the first weakly-supervised results on the AVA dataset and state-of-the-art results among weakly-supervised methods on UCF101-24.
Research Areas
