Abstract

In this work, we propose a novel Reversible Recursive Instance-level Object Segmentation (R2-IOS) framework toaddress the challenging instance-level object segmentationtask. R2-IOS consists of a reversible proposal refinement sub-network that predicts bounding box offsets for refiningthe object proposal locations, and an instance-level segmentation sub-network that generates the foreground mask of the dominant object instance in each proposal. By being recursive, R2-IOS iteratively optimizes the two subnetworks during joint training, in which the refined object proposals and improved segmentation predictions are alternately fed into each other to progressively increase the network capabilities. By being reversible, the proposal refinement sub-network adaptively determines an optimal numberof refinement iterations required for each proposal during both training and testing. Furthermore, to handle multiple overlapped instances within a proposal, an instance-aware denoising autoencoder is introduced into the segmentation sub-network to distinguish the dominant object from other distracting instances. Extensive experiments on the challenging PASCAL VOC 2012 benchmark well demonstrate the superiority of R2-IOS over other state-of-the-art methods. In particular, the APr over 20 classes at 0.5 IoU achieves 66.7%, which significantly outperforms the results of 58.7% by PFN [17] and 46.3% by [22].