  In this work, we present a novel and effective framework to facilitate object
detection with the instance-level segmentation information that is only
supervised by bounding box annotation. Starting from the joint object detection
and instance segmentation network, we propose to recursively estimate the
pseudo ground-truth object masks from the instance-level object segmentation
network training, and then enhance the detection network with top-down
segmentation feedbacks. The pseudo ground truth mask and network parameters are
optimized alternatively to mutually benefit each other. To obtain the promising
pseudo masks in each iteration, we embed a graphical inference that
incorporates the low-level image appearance consistency and the bounding box
annotations to refine the segmentation masks predicted by the segmentation
network. Our approach progressively improves the object detection performance
by incorporating the detailed pixel-wise information learned from the
weakly-supervised segmentation network. Extensive evaluation on the detection
task in PASCAL VOC 2007 and 2012 [12] verifies that the proposed approach is
effective.
