InstanceCut: from Edges to Instances with MultiCut

TL;DR

InstanceCut introduces a novel approach for instance-aware semantic segmentation by combining semantic segmentation and edge detection within a MultiCut framework, achieving state-of-the-art results on CityScapes.

Contribution

The paper presents a new paradigm that integrates semantic segmentation and edge detection through MultiCut for improved instance segmentation.

Findings

Achieves top performance on CityScapes dataset.

Performs well on rare object classes.

Conceptually simple yet highly effective.

Abstract

This work addresses the task of instance-aware semantic segmentation. Our key motivation is to design a simple method with a new modelling-paradigm, which therefore has a different trade-off between advantages and disadvantages compared to known approaches. Our approach, we term InstanceCut, represents the problem by two output modalities: (i) an instance-agnostic semantic segmentation and (ii) all instance-boundaries. The former is computed from a standard convolutional neural network for semantic segmentation, and the latter is derived from a new instance-aware edge detection model. To reason globally about the optimal partitioning of an image into instances, we combine these two modalities into a novel MultiCut formulation. We evaluate our approach on the challenging CityScapes dataset. Despite the conceptual simplicity of our approach, we achieve the best result among all published methods, and perform particularly well for rare object classes.