Weakly supervised human body detection under arbitrary poses

In this work we study the problem of weakly supervised human body detection under difficult poses (e.g., multiview and/or arbitrary poses) within the framework of multi-instance learning (MIL). We first point out the existence of the so-called “vanishing gradient” problem in MIL with a noisy-or rule as its bagging model. This is mainly due to the independence assumption of the noisy-or rule, which significantly reducing the magnitude of gradient under a weak initial instance-level model. To address this issue, we propose an iterative selective MIL method in which 1) the noisy-or rule is replaced with the max rule and only a few instances are included for MIL learning for each bag and for each time, and 2) prior knowledge about the positive instances in terms of few fully supervised samples are employed to improve the robustness. The method is shown to outperform the previous state-of-the-art methods by over 20.0% in accuracy. Finally, we present a new large-scale data set called MPHB (Multiple Poses Human Body) for human body detection under arbitrary poses.