  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 low-quality 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.
