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20162022
most citedSemantic Instance Segmentation with a Discriminative Loss Function

444 citations · 1.6k across the 80 of their papers we have counts for

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Showing 2017Show all

14 papers · 1 filter

cs.CV20176 cited

Error Correction for Dense Semantic Image Labeling

Yu-Hui Huang, Xu Jia, Stamatios Georgoulis +2

Pixelwise semantic image labeling is an important, yet challenging, task with many applications. Typical approaches to tackle this problem involve either the training of deep netwo…

cs.CV201711 cited

Iterative Deep Learning for Network Topology Extraction

Carles Ventura, Jordi Pont-Tuset, Sergi Caelles +2

This paper tackles the task of estimating the topology of filamentary networks such as retinal vessels and road networks. Building on top of a global model that performs a dense se…

cs.CV20178 cited

Face Translation between Images and Videos using Identity-aware CycleGAN

Zhiwu Huang, Bernhard Kratzwald, Danda Pani Paudel +2

This paper presents a new problem of unpaired face translation between images and videos, which can be applied to facial video prediction and enhancement. In this problem there exi…

cs.CV20173 cited

Dense 3D Regression for Hand Pose Estimation

Chengde Wan, Thomas Probst, Luc Van Gool +1

We present a simple and effective method for 3D hand pose estimation from a single depth frame. As opposed to previous state-of-the-art methods based on holistic 3D regression, our…

cs.CV2017187 cited

Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification

Ali Diba, Mohsen Fayyaz, Vivek Sharma +4

The work in this paper is driven by the question how to exploit the temporal cues available in videos for their accurate classification, and for human action recognition in particu…

cs.CV2017313 cited

WebVision Database: Visual Learning and Understanding from Web Data

Wen Li, Limin Wang, Wei Li +2

In this paper, we present a study on learning visual recognition models from large scale noisy web data. We build a new database called WebVision, which contains more than mi…