TS2C: Tight Box Mining with Surrounding
Segmentation Context for Weakly Supervised
Object Detection
Abstract. This work provides a simple approach to discover tight object bounding boxes with only image-level supervision, called Tight box
mining with Surrounding Segmentation Context (TS2C). We observe
that object candidates mined through current multiple instance learning methods are usually trapped to discriminative object parts, rather
than the entire object. TS2C leverages surrounding segmentation context derived from weakly-supervised segmentation to suppress such lowquality distracting candidates and boost the high-quality ones. Specifically, TS2C is developed based on two key properties of desirable bounding
boxes: 1) high purity, meaning most pixels in the box are with high object
response, and 2) high completeness, meaning the box covers high object
response pixels comprehensively. With such novel and computable criteria, more tight candidates can be discovered for learning a better object
detector. With TS2C, we obtain 48.0% and 44.4% mAP scores on VOC
2007 and 2012 benchmarks, which are the new state-of-the-arts