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
More filters
Posted Content
From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network
TL;DR: PointCloudDet3D as mentioned in this paper proposes a RoI-aware point cloud pooling module to encode the geometry-specific features of each 3D proposal, which achieves state-of-the-art performance on KITTI 3D object detection dataset.
Book ChapterDOI
Deep Learning in the Wild
Thilo Stadelmann,Mohammadreza Amirian,Ismail Arabaci,Marek Arnold,Gilbert François Duivesteijn,Ismail Elezi,Melanie Geiger,Stefan Lörwald,Benjamin Bruno Meier,Katharina Rombach,Lukas Tuggener +10 more
TL;DR: This paper explored the specific challenges arising in the realm of real world tasks, based on case studies from research & development in conjunction with industry, and extracts lessons learned from them. But they did not provide any guidance on how to make them work in practice.
Journal ArticleDOI
The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning
Steffen Wolf,Alberto Bailoni,Constantin Pape,Nasim Rahaman,Anna Kreshuk,Ullrich Köthe,Fred A. Hamprecht +6 more
TL;DR: The Mutex Watershed is proposed, an efficient algorithm for graph partitioning that can accommodate not only attractive but also repulsive cues, allowing it to find a previously unspecified number of segments without the need for explicit seeds or a tunable threshold.
Journal ArticleDOI
Leveraging Instance-, Image- and Dataset-Level Information for Weakly Supervised Instance Segmentation
TL;DR: Li et al. as mentioned in this paper proposed a multiple instance learning (MIL) framework, which can be trained in an end-to-end manner using training images with image-level labels.
Journal ArticleDOI
BshapeNet: Object detection and instance segmentation with bounding shape masks
TL;DR: BshapeNet as discussed by the authors proposes a modularizable component that can predict the boundary shapes and boxes of an image, along with a new masking scheme for improving object detection and instance segmentation.
References
More filters
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Proceedings ArticleDOI
Fully convolutional networks for semantic segmentation
TL;DR: The key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.
Proceedings ArticleDOI
Pyramid Scene Parsing Network
TL;DR: This paper exploits the capability of global context information by different-region-based context aggregation through the pyramid pooling module together with the proposed pyramid scene parsing network (PSPNet) to produce good quality results on the scene parsing task.
Proceedings Article
Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot,Yoshua Bengio +1 more
TL;DR: The objective here is to understand better why standard gradient descent from random initialization is doing so poorly with deep neural networks, to better understand these recent relative successes and help design better algorithms in the future.
Proceedings ArticleDOI
The Cityscapes Dataset for Semantic Urban Scene Understanding
Marius Cordts,Mohamed Omran,Sebastian Ramos,Timo Rehfeld,Markus Enzweiler,Rodrigo Benenson,Uwe Franke,Stefan Roth,Bernt Schiele +8 more
TL;DR: This work introduces Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling, and exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity.