Learning Spatial Transforms for Refining Object Segment Proposals
We address the problem of object segment proposal generation, which is a critical step in many instance-level semantic segmentation and scene understanding pipelines. In contrast to prior works that predict binary segment masks from images, we take an alternative refinement approach to improve the quality of a given segment candidate pool. In particular, we propose an efficient deep network that learns 2D spatial transforms to warp an initial object mask towards nearby object region. We formulate this segment refinement task as a regression problem and design a novel feature pooling strategy in our deep network to predict an affine transformation for each object mask. We evaluate our method extensively on two challenging public benchmarks and apply our refinement network to three different initial segment proposal settings. Our results show sizable improvements in average recall across all the settings, achieving the state-of-the-art performances.