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
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Rethinking Text Segmentation: A Novel Dataset and A Text-Specific Refinement Approach
TL;DR: This work proposes TextSeg, a large-scale fine-annotated text dataset with six types of annotations, and introduces Text Refinement Network (TexRNet), a novel text segmentation approach that adapts to the unique properties of text, e.g. non-convex boundary, diverse texture, etc., which often impose burdens on traditional segmentation models.
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SCNet: Training Inference Sample Consistency for Instance Segmentation
TL;DR: Sample Consistency Network (SCNet) as mentioned in this paper proposes an architecture referred to as sample consistency network to ensure that the IoU distribution of the samples at training time is close to that at inference time and incorporates feature relay and utilizes global contextual information to further reinforce the reciprocal relationships among classifying, detecting and segmenting sub-tasks.
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Deep-recursive residual network for image semantic segmentation
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Amodal Segmentation Based on Visible Region Segmentation and Shape Prior
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SSAP: Single-Shot Instance Segmentation With Affinity Pyramid
TL;DR: This work proposes a single-shot proposal-free instance segmentation method that requires only one single pass for prediction and is based on learning an affinity pyramid, which computes the probability that two pixels belong to the same instance in a hierarchical manner.
References
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Proceedings ArticleDOI
The Cityscapes Dataset for Semantic Urban Scene Understanding
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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.