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Effective integration of local and global contextual information is crucialfor semantic segmentation and dense image labeling. We develop twoencoder-decoder based deep learning architectures to address this problem. Wefirst propose a network architecture called Label Refinement Network (LRN) thatpredicts segmentation labels in a coarse-to-fine fashion at several spatialresolutions. In this network, we also define loss functions at several stagesto provide supervision at different stages of training. However, there arelimits to the quality of refinement possible if ambiguous information is passedforward. In order to address this issue, we also propose Gated FeedbackRefinement Network (G-FRNet) that addresses this limitation. Initially, G-FRNetmakes a coarse-grained prediction which it progressively refines to recoverdetails by effectively integrating local and global contextual informationduring the refinement stages. This is achieved by gate units proposed in thiswork, that control information passed forward in order to resolve theambiguity. Experiments were conducted on four challenging dense labelingdatasets (CamVid, PASCAL VOC 2012, Horse-Cow Parsing, PASCAL-Person-Part, andSUN-RGBD). G-FRNet achieves state-of-the-art semantic segmentation results onthe CamVid and Horse-Cow Parsing datasets and produces results competitive withthe best performing approaches that appear in the literature for the otherthree datasets.

Gated Feedback Refinement Network for Coarse-to-Fine Dense Semantic Image Labeling