  Most of existing detection pipelines treat object proposals independently and
predict bounding box locations and classification scores over them separately.
However, the important semantic and spatial layout correlations among proposals
are often ignored, which are actually useful for more accurate object
detection. In this work, we propose a new EM-like group recursive learning
approach to iteratively refine object proposals by incorporating such context
of surrounding proposals and provide an optimal spatial configuration of object
detections. In addition, we propose to incorporate the weakly-supervised object
segmentation cues and region-based object detection into a multi-stage
architecture in order to fully exploit the learned segmentation features for
better object detection in an end-to-end way. The proposed architecture
consists of three cascaded networks which respectively learn to perform
weakly-supervised object segmentation, object proposal generation and recursive
detection refinement. Combining the group recursive learning and the
multi-stage architecture provides competitive mAPs of 78.6% and 74.9% on the
PASCAL VOC2007 and VOC2012 datasets respectively, which outperforms many
well-established baselines [10] [20] significantly.
