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
Multi-Task Learning for Segmentation of Building Footprints with Deep Neural Networks
TL;DR: This paper addresses the problem of preserving semantic segmentation boundaries in high-resolution satellite imagery by introducing a novel multi-task loss that leverages multiple output representations of the segmentation mask and biases the network to focus more on pixels near boundaries.
Journal ArticleDOI
EfficientPS: Efficient Panoptic Segmentation
Rohit Mohan,Abhinav Valada +1 more
TL;DR: The Efficient Panoptic Segmentation (EfficientPS) architecture is introduced that consists of a shared backbone which efficiently encodes and fuses semantically rich multi-scale features and incorporates a new semantic head that aggregates fine and contextual features coherently.
Posted Content
DeeperLab: Single-Shot Image Parser
Tien-Ju Yang,Maxwell D. Collins,Yukun Zhu,Jyh-Jing Hwang,Ting Liu,Xiao Zhang,Vivienne Sze,George Papandreou,Liang-Chieh Chen +8 more
TL;DR: The proposed DeeperLab image parser performs whole image parsing with a significantly simpler, fully convolutional approach that jointly addresses the semantic and instance segmentation tasks in a single-shot manner, resulting in a streamlined system that better lends itself to fast processing.
Journal ArticleDOI
Proposal-Free Network for Instance-Level Object Segmentation
TL;DR: Li et al. as mentioned in this paper proposed a Proposal-Free Network (PFN) to address the instance-level object segmentation problem, which outputs the number of instances of different categories and the pixel-level information on i) the coordinates of the instance bounding box each pixel belongs to, and ii) the confidences of different classes for each pixel, based on pixel-to-pixel deep convolutional neural network.
Proceedings ArticleDOI
MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers
TL;DR: MaX-DeepLab as discussed by the authors proposes a mask transformer to directly predict class-labeled masks with mask transformer, and is trained with a panoptic quality inspired loss via bipartite matching.
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