Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable Approach

Ahmed Abbas, Paul Swoboda

Keywords: Combinatorial optimization, Backpropagation, Panoptic segmentation, end-to-end learning, CNNs, Gradient estimation, Fully differentiable

TL;DR: Show insights and benefits of training a pipeline containing neural network and large scale combinatorial optimization problem for panoptic segmentation

Abstract:

We propose a fully differentiable 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 by backpropagating through the optimization problem w.r.t. comparable approaches on Cityscapes and COCO datasets. Overall, our approach of combinatorial optimization for panoptic segmentation (COPS) shows the utility of using optimization in tandem with deep learning in a challenging large scale real-world problem and showcases benefits and insights into training such an architecture.

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Code: https://github.com/aabbas90/COPS/