Combinatorial Optimization for Panoptic Segmentation: An End-to-End Trainable Approach

• URL: http://arxiv.org/abs/2106.03188v1
• Date: Sun, 6 Jun 2021 17:39:13 GMT
• Title: Combinatorial Optimization for Panoptic Segmentation: An End-to-End Trainable Approach
• Authors: Ahmed Abbas, Paul Swoboda
• Abstract summary: We propose an end-to-end trainable architecture for simultaneous semantic and instance segmentation. Our approach shows the utility of using optimization in tandem with deep learning in a challenging large scale real-world problem.
• Score: 23.281726932718232
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose an end-to-end trainable architecture for simultaneous semantic and instance segmentation (a.k.a. panoptic segmentation) consisting of a convolutional neural network and an asymmetric multiway cut problem solver. The latter solves a combinatorial optimization problem that elegantly incorporates semantic and boundary predictions to produce a panoptic labeling. Our formulation allows to directly maximize a smooth surrogate of the panoptic quality metric by backpropagating the gradient through the optimization problem. Experimental evaluation shows improvement of end-to-end learning w.r.t. comparable approaches on Cityscapes and COCO datasets. Overall, our approach shows the utility of using combinatorial optimization in tandem with deep learning in a challenging large scale real-world problem and showcases benefits and insights into training such an architecture end-to-end.

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