Abstract

In this work, we propose a novel Reversible Recursive Instance-level Object Segmentation (R2-IOS) framework to address the challenging instance-level object segmentation task. R2-IOS consists of a reversible proposal refinement sub-network that predicts bounding box offsets for refining the 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 number of 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].

Keywords

SegmentationComputer sciencePascal (unit)Artificial intelligenceBenchmark (surveying)Object (grammar)Bounding overwatchMinimum bounding boxPattern recognition (psychology)Image segmentationImage (mathematics)

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Publication Info

Year
2016
Type
preprint
Volume
111
Pages
633-641
Citations
50
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Closed

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Cite This

Xiaodan Liang, Yunchao Wei, Xiaohui Shen et al. (2016). Reversible Recursive Instance-Level Object Segmentation. , 111 , 633-641. https://doi.org/10.1109/cvpr.2016.75

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DOI
10.1109/cvpr.2016.75