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所属单位：计算机科学与技术学院/人工智能学院/软件学院

发表刊物：PATTERN RECOGNITION

关键字：Weakly supervised learning Human detection Selective Weakly Supervised Detection (SWSD) Multi-instance learning (MIL)

摘要：In this paper we study the problem of weakly supervised human detection under arbitrary poses within the framework of multi-]instance learning (MIL). Our contributions are threefold: (1) we first show that in the context of weakly supervised learning, some commonly used bagging tools in MIL such as the Noisy-]OR model or the ISR model tend to suffer from the problem of gradient magnitude reduction when the initial instance level detector is weak and/or when there exist large number of negative proposals, resulting in extremely inefficient use of training examples. We hence advocate the use of more robust and simple max-]pooling rule or average rule under such circumstances; (2) we propose a new Selective Weakly Supervised Detection (SWSD) algorithm, which is shown to outperform several previous state-of-the-art weakly supervised methods; (3) finally, we identify several crucial factors that may significantly influence the performance, such as the usefulness of a small amount of supervision information, the need of relatively higher RoP (Ratio of Positive Instances), and so on these factors are shown to benefit the MIL-]based weakly supervised detector but are less studied in the previous literature. We also annotate a new large-scale data set called LSP/MPII-MPHB (Multiple Poses Human Body), in which and another popular benchmark dataset we demonstrate the superiority of the proposed method compared to several previous state-of-the-art methods.

ISSN号：0031-3203

是否译文：否

发表时间：2017-05-01

合写作者：Cai, Yawei,Tan, Xiaosong

通讯作者：谭晓阳