A Self-Training Approach for Point-Supervised Object Detection and Counting in Crowds

25 Jul 2020 Yi Wang Junhui Hou Xinyu Hou Lap-Pui Chau

In this paper, we propose a novel self-training approach which enables a typical object detector trained only with point-level annotations (i.e., objects are labeled with points) to estimate both the center points and sizes of crowded objects. Specifically, during training we utilize the available point annotations to directly supervise the estimation of the center points of objects... (read more)

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