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.

Identifying the same persons across different views plays an important role in many vision applications. In this paper, we study this important problem, denoted as Multi-view Multi-Human Association (MvMHA), on multi-view images that are taken by different cameras at the same time. Different from previous works on human association across two views, this paper is focused on more general and challenging scenarios of more than two views, and none of these views are fixed or priorly known. In addition, each involved person may be present in all the views or only a subset of views, which are also not priorly known. We develop a new end-to-end deep-network based framework to address this problem. First, we use an appearance-based deep network to extract the feature of each detected subject on each image. We then compute pairwise-similarity scores between all the detected subjects and construct a comprehensive affinity matrix. Finally, we propose a Deep Assignment Network (DAN) to transform the affinity matrix into an assignment matrix, which provides a binary assignment result for MvMHA. We build both a synthetic dataset and a real image dataset to verify the effectiveness of the proposed method. We also test the trained network on other three public datasets, resulting in very good cross-domain performance.