关键词: 弱监督, 人体检测, 任意姿态, 多示例学习

Abstract:  The problem of weakly supervised human body detection under difficult poses (e.g., multi-view and/or  arbitrary poses) is studied. Most current methods on human body detection focuse only on a few common human body poses with human body in upright positions, while in the real world human bodies may exhibit very rich pose variations (e.g., when people are bending, sleeping or sitting). This not only imposes great challenges on the task of human detection, but also makes the job of manual annotation even more difficult, and usually only weak annotations are available in practice. The multiple instance learning method relaxes the requirements of accurate labeling and hence is commonly used to address the task. However, it is sensitive to the quality of positive instances and the settings of some model parameters such as the strategy to fuse the instance-level conditional probability into a bag-level one. This paper presents a comprehensive and in-depth empirical method of these important but less studied   issues on the person dataset of Pascal VOC 2007, and proposes a new selective weakly supervised detection algorithm (SWSD). Experiments demonstrate that with only a few fully supervised samples, the performance of weakly supervised human body detection can be significantly improved under the multiple instance learning framework.

Key words: weakly supervision, human body detection, arbitrary poses, multiple instance learning