Weakly Supervised Instance Action Recognition

We study the novel problem of weakly supervised instance action recognition (WSiAR) in multi-person (crowd) scenes. We specifically aim to recognize the action of each subject in the crowd, for which we propose the use of a weakly supervised method, considering the expense of large-scale annotations for training. This problem is of great practical value for video surveillance and sports scene analysis. To this end, we investigated and designed a series of weak annotations for the supervision of weakly supervised instance action recognition (WSiAR). We propose two categories of weak label settings, bag labels and sparse labels, to significantly reduce the number of labels. Based on the former, we propose a novel sub-block-aware multi-instance learning (MIL) loss to obtain more effective information from weak labels during training. With respect to the latter, we propose a pseudo label generation strategy for extending sparse labels. This enables our method to achieve results comparable to those of fully supervised methods but with significantly fewer annotations. The experimental results on two benchmarks verified the rationality of the problem definition and effectiveness of the proposed weakly supervised training method in solving our problem.