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C-WSL: Count-guided Weakly Supervised Localization

Mingfei Gao
Ang Li
Ruichi Yu
Vlad I. Morariu
Larry S. Davis
European Conference on Computer Vision (ECCV) (2018) (to appear)
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Abstract

We introduce a count-guided weakly supervised localization (C-WSL) framework with per-class object count as an additional form of image-level 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 at training time, and improves WSL by training with the selected regions. To demonstrate the effectiveness of C-WSL, we integrate object count supervision into two WSL architectures and conduct extensive experiments on Pascal VOC2007 and VOC2012. Experimental results show that C-WSL leads to large improvements in WSL detection performance and that the proposed approach significantly outperforms the state-of-the-art methods.