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Panoptic segmentation is a scene parsing task which unifies semanticsegmentation and instance segmentation into one single task. However, thecurrent state-of-the-art studies did not take too much concern on inferencetime. In this work, we propose an Efficient Panoptic Segmentation Network(EPSNet) to tackle the panoptic segmentation tasks with fast inference speed.Basically, EPSNet generates masks based on simple linear combination ofprototype masks and mask coefficients. The light-weight network branches forinstance segmentation and semantic segmentation only need to predict maskcoefficients and produce masks with the shared prototypes predicted byprototype network branch. Furthermore, to enhance the quality of sharedprototypes, we adopt a module called "cross-layer attention fusion module",which aggregates the multi-scale features with attention mechanism helping themcapture the long-range dependencies between each other. To validate theproposed work, we have conducted various experiments on the challenging COCOpanoptic dataset, which achieve highly promising performance with significantlyfaster inference speed (53ms on GPU).

EPSNet: Efficient Panoptic Segmentation Network with Cross-layer Attention Fusion