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.

本文介绍了计数引导的弱监督定位(C-WSL)，使用每类物体数量作为一种新的监督形式来改善弱监督定位(WSL)。C-WSL使用一种简单的基于计数的区域选择算法来选择高质量的区域，每个区域在训练期间覆盖一个单独的对象实例，并通过训练选择的区域来提高现有的WSL方法，实验结果表明，C-WSL可以显著提高WSL性能，使其显著优于现有的最先进方法。同时，我们降低了注释时间，相对于中心点击和边界框注释，减少了超过2倍和38倍。

C-WSL: 计数引导的弱监督定位