C-WSL: Count-guided Weakly Supervised Localization

Mingfei Gao, Ang Li, Ruichi Yu, Vlad I. Morariu, Larry S. Davis; Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 152-168

We introduce count-guided weakly supervised localization (C-WSL), an approach that uses per-class object count as a new form of supervision to improve weakly supervised localization (WSL). C-WSL uses a simple count-based region selection algorithm to select high-quality regions, each of which covers a single object instance during training, and improves existing WSL methods by training with the selected regions. To demonstrate the effectiveness of C-WSL, we integrate it into two WSL architectures and conduct extensive experiments on VOC2007 and VOC2012. Experimental results show that C-WSL leads to large improvements in WSL and that the proposed approach significantly outperforms the state-of-the-art methods. The results of annotation experiments on VOC2007 suggest that a modest extra time is needed to obtain per-class object counts compared to labeling only object categories in an image. Furthermore, we reduce the annotation time by more than 2 times and 38 times compared to center-click and bounding-box annotations.