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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Proceedings ArticleDOI
Instance Segmentation Combined CMT and Swin Transformer in Driving Scenes
TL;DR: Wang et al. as mentioned in this paper combine the advantages of CMT and swin transformer to enrich feature extraction and build a framework that used the new backbone to achieve instance segmentation. But usually, CNN and Transformer are utilized independently.
Proceedings ArticleDOI
Instance Segmentation by Learning Pixel Neighbour Relations with a CNN
TL;DR: This paper proposes an approach to instance segmentation based on deep learning that can associate freestanding (unconnected) areas of the same instance and shows that these long-range neighbour relations result in higher accuracies for all classes.
Proceedings ArticleDOI
SCTS: Instance Segmentation of Single Cells Using a Transformer-Based Semantic-Aware Model and Space-Filling Augmentation
TL;DR: SCTS as discussed by the authors utilizes a Swin Transformer as its backbone, combining the global modeling capabilities of a Transformer and the local modelling capabilities of CNN to ensure model adaptability to different cell sizes, shapes, and textures.
Journal ArticleDOI
Image segmentation with boundary-to-pixel direction and magnitude based on watershed and attention mechanism
TL;DR: In this article , an improved image segmentation algorithm with boundary-to-pixel direction and magnitude (IS-BPDM) is proposed to deal with small regions segmentation while keeping the accuracy of edge segmentation.
Proceedings ArticleDOI
More Competitive Feature Extraction Network for Instance Segmentation
TL;DR: Improvements on Mask R-CNN and Squeeze-and-Excitation feature model are proposed, which greatly reduce false detection and missed detection and achieve significant improvements in the MS COCO dataset.
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