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
PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation
TL;DR: This paper designs a two-branch network to extract point features and predict semantic labels and offsets, for shifting each point towards its respective instance centroid, and presents PointGroup, a new end-to-end bottom-up architecture specifically focused on better grouping the points by exploring the void space between objects.
Journal ArticleDOI
YOLACT++: Better Real-time Instance Segmentation.
TL;DR: A simple, fully-convolutional model for real-time instance segmentation that achieves competitive results on MS COCO evaluated on a single Titan Xp, which is significantly faster than any previous state-of-the-art approach.
Proceedings ArticleDOI
Learning to Segment Every Thing
TL;DR: A new partially supervised training paradigm is proposed, together with a novel weight transfer function, that enables training instance segmentation models on a large set of categories all of which have box annotations, but only a small fraction ofWhich have mask annotations.
Proceedings ArticleDOI
Attention-Guided Unified Network for Panoptic Segmentation
TL;DR: In this article, an attention-guided unified network (AUNet) is proposed for panoptic segmentation, in which foreground objects provide complementary cues to assist background understanding, and two sources of attentions are added to the foreground objects to provide object-level and pixel-level attentions, respectively.
Posted Content
Panoptic Segmentation
TL;DR: Panoptic segmentation as discussed by the authors unifies the typically distinct tasks of semantic segmentation and instance segmentation, and proposes a novel panoptic quality (PQ) metric that captures performance for all classes (stuff and things) in an interpretable and unified manner.
References
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