Deep Watershed Transform for Instance Segmentation
Min Bai,Raquel Urtasun +1 more
- pp 2858-2866
TLDR
This paper presents a simple yet powerful end-to-end convolutional neural network that achieves more than double the performance over the state-of-the-art on the challenging Cityscapes Instance Level Segmentation task.Abstract:
Most contemporary approaches to instance segmentation use complex pipelines involving conditional random fields, recurrent neural networks, object proposals, or template matching schemes. In this paper, we present a simple yet powerful end-to-end convolutional neural network to tackle this task. Our approach combines intuitions from the classical watershed transform and modern deep learning to produce an energy map of the image where object instances are unambiguously represented as energy basins. We then perform a cut at a single energy level to directly yield connected components corresponding to object instances. Our model achieves more than double the performance over the state-of-the-art on the challenging Cityscapes Instance Level Segmentation task.read more
Citations
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MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers.
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Proceedings ArticleDOI
Deep Snake for Real-Time Instance Segmentation
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TL;DR: For the first time, a bottom-up approach could deliver state-of-the-art results on panoptic segmentation, and performs on par with several top-down approaches on the challenging COCO dataset.
Proceedings ArticleDOI
Fast Interactive Object Annotation With Curve-GCN
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Proceedings ArticleDOI
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