Weakly supervised spatial-temporal attention network driven by tracking and consistency loss for action detection

This study proposes a novel network model for video action tube detection. This model is based on a location-interactive weakly supervised spatial-temporal attention mechanism driven by multiple loss functions. It is especially costly and time consuming to annotate every target location in video frames. Thus, we first propose a cross-domain weakly supervised learning method with a spatial-temporal attention mechanism for action tube detection. In source domain, we trained a newly designed multi-loss spatial-temporal attention-convolution network on the source data set, which has both object location and classification annotations. In target domain, we introduced internal tracking loss and neighbor-consistency loss; we trained the network with the pre-trained model on the target data set, which only has inaccurate action temporal positions. Although this is a location-unsupervised method, its performance outperforms typical weakly supervised methods, and even shows comparable results with some recent fully supervised methods. We also visualize the activation maps, which reveal the intrinsic reason behind the higher performance of the proposed method.