  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.
