Hierarchical Lov\'asz Embeddings for Proposal-free Panoptic Segmentation

• URL: http://arxiv.org/abs/2106.04555v1
• Date: Tue, 8 Jun 2021 17:43:54 GMT
• Title: Hierarchical Lov\'asz Embeddings for Proposal-free Panoptic Segmentation
• Authors: Tommi Kerola, Jie Li, Atsushi Kanehira, Yasunori Kudo, Alexis Vallet, Adrien Gaidon
• Abstract summary: State-of-the-art panoptic segmentation methods use complex models with a distinct stream for each task. We propose Hierarchical Lov'asz Embeddings, per pixel feature vectors that simultaneously encode instance- and category-level discriminative information. Our model achieves state-of-the-art results compared to existing proposal-free panoptic segmentation methods on Cityscapes, COCO, and Mapillary Vistas.
• Score: 25.065380488503262
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Panoptic segmentation brings together two separate tasks: instance and semantic segmentation. Although they are related, unifying them faces an apparent paradox: how to learn simultaneously instance-specific and category-specific (i.e. instance-agnostic) representations jointly. Hence, state-of-the-art panoptic segmentation methods use complex models with a distinct stream for each task. In contrast, we propose Hierarchical Lov\'asz Embeddings, per pixel feature vectors that simultaneously encode instance- and category-level discriminative information. We use a hierarchical Lov\'asz hinge loss to learn a low-dimensional embedding space structured into a unified semantic and instance hierarchy without requiring separate network branches or object proposals. Besides modeling instances precisely in a proposal-free manner, our Hierarchical Lov\'asz Embeddings generalize to categories by using a simple Nearest-Class-Mean classifier, including for non-instance "stuff" classes where instance segmentation methods are not applicable. Our simple model achieves state-of-the-art results compared to existing proposal-free panoptic segmentation methods on Cityscapes, COCO, and Mapillary Vistas. Furthermore, our model demonstrates temporal stability between video frames.

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