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Panoptic segmentation aims to perform instance segmentation for foregroundinstances and semantic segmentation for background stuff simultaneously. Thetypical top-down pipeline concentrates on two key issues: 1) how to effectivelymodel the intrinsic interaction between semantic segmentation and instancesegmentation, and 2) how to properly handle occlusion for panopticsegmentation. Intuitively, the complementarity between semantic segmentationand instance segmentation can be leveraged to improve the performance. Besides,we notice that using detection/mask scores is insufficient for resolving theocclusion problem. Motivated by these observations, we propose a novel deeppanoptic segmentation scheme based on a bidirectional learning pipeline.Moreover, we introduce a plug-and-play occlusion handling algorithm to dealwith the occlusion between different object instances. The experimental resultson COCO panoptic benchmark validate the effectiveness of our proposed method.Codes will be released soon at https://github.com/Mooonside/BANet.

BANet: Bidirectional Aggregation Network with Occlusion Handling for Panoptic Segmentation