Temporal Dropout for Weakly Supervised Action Localization

Abstract

Weakly supervised action localization is a challenging problem in video understanding and action recognition. Existing models usually formulate the training process as direct classification using video-level supervision. They tend to only locate the most discriminative parts of action instances and produce temporally incomplete detection results. A natural solution for this problem, the adversarial erasing strategy, is to remove such parts from training so that models can attend to complementary parts. Previous works do it in an offline and heuristic way. They adopt a multi-stage pipeline, where discriminative regions are determined and erased under the guidance of detection results from last stage. Such a pipeline can be both ineffective and inefficient, possibly hindering the overall performance. On the contrary, we combine adversarial erasing with dropout mechanism and propose a Temporal Dropout Module that learns where to remove in a data-driven and online manner. This plug-and-play module is trained without iterative stages, which not only simplifies the pipeline but also makes the regularization during training easier and more adaptive. Experiments show that the proposed method outperforms previous erasing-based methods by a large margin. More importantly, it achieves universal improvement when plugged into various direct classification methods and obtains state-of-the-art performance.