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Most implemented papers

COCO-Stuff: Thing and Stuff Classes in Context

nightrome/cocostuff CVPR 2018

To understand stuff and things in context we introduce COCO-Stuff, which augments all 164K images of the COCO 2017 dataset with pixel-wise annotations for 91 stuff classes.

Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture

yhlleo/DeepSegmentor ICCV 2015

In this paper we address three different computer vision tasks using a single basic architecture: depth prediction, surface normal estimation, and semantic labeling.

Inner and Inter Label Propagation: Salient Object Detection in the Wild

hli2020/lps_tip15 27 May 2015

For most natural images, some boundary superpixels serve as the background labels and the saliency of other superpixels are determined by ranking their similarities to the boundary labels based on an inner propagation scheme.

Learning to Segment Object Candidates

facebookresearch/deepmask NeurIPS 2015

Recent object detection systems rely on two critical steps: (1) a set of object proposals is predicted as efficiently as possible, and (2) this set of candidate proposals is then passed to an object classifier.

Superpixels: An Evaluation of the State-of-the-Art

davidstutz/superpixel-benchmark 6 Dec 2016

As such, and due to their quick adoption in a wide range of applications, appropriate benchmarks are crucial for algorithm selection and comparison.

Superpixel Sampling Networks

NVlabs/ssn_superpixels ECCV 2018

Superpixels provide an efficient low/mid-level representation of image data, which greatly reduces the number of image primitives for subsequent vision tasks.

Automatic skin lesion segmentation on dermoscopic images by the means of superpixel merging

dipaco/mole-classification 21 Aug 2018

We present a superpixel-based strategy for segmenting skin lesion on dermoscopic images.

Let's take a Walk on Superpixels Graphs: Deformable Linear Objects Segmentation and Model Estimation

m4nh/ariadne 10 Oct 2018

While robotic manipulation of rigid objects is quite straightforward, coping with deformable objects is an open issue.

A Curriculum Domain Adaptation Approach to the Semantic Segmentation of Urban Scenes

YangZhang4065/AdaptationSeg 24 Dec 2018

Hence, we propose a curriculum-style learning approach to minimizing the domain gap in urban scene semantic segmentation.

Superpixel Segmentation with Fully Convolutional Networks

fuy34/superpixel_fcn CVPR 2020

In computer vision, superpixels have been widely used as an effective way to reduce the number of image primitives for subsequent processing.