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20162021
most citedComplementary Pseudo Labels For Unsupervised Domain Adaptation On Person Re-identification

87 citations · 593 across the 31 of their papers we have counts for

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26 papers · 1 filter

cs.CV202128 cited

ES-Net: Erasing Salient Parts to Learn More in Re-Identification

Dong Shen, Shuai Zhao, Jinming Hu +3

As an instance-level recognition problem, re-identification (re-ID) requires models to capture diverse features. However, with continuous training, re-ID models pay more and more a…

cs.CV202187 cited

Complementary Pseudo Labels For Unsupervised Domain Adaptation On Person Re-identification

Hao Feng, Minghao Chen, Jinming Hu +3

In recent years, supervised person re-identification (re-ID) models have received increasing studies. However, these models trained on the source domain always suffer dramatic perf…

cs.CV2020

Geometry-based Occlusion-Aware Unsupervised Stereo Matching for Autonomous Driving

Liang Peng, Dan Deng, Deng Cai

Recently, there are emerging many stereo matching methods for autonomous driving based on unsupervised learning. Most of them take advantage of reconstruction losses to remove depe…

cs.CV20205 cited

Apparel-invariant Feature Learning for Apparel-changed Person Re-identification

Zhengxu Yu, Yilun Zhao, Bin Hong +5

With the rise of deep learning methods, person Re-Identification (ReID) performance has been improved tremendously in many public datasets. However, most public ReID datasets are c…

cs.CV20203 cited

Learning to Caricature via Semantic Shape Transform

Wenqing Chu, Wei-Chih Hung, Yi-Hsuan Tsai +4

Caricature is an artistic drawing created to abstract or exaggerate facial features of a person. Rendering visually pleasing caricatures is a difficult task that requires professio…

cs.CV2020

RESA: Recurrent Feature-Shift Aggregator for Lane Detection

Tu Zheng, Hao Fang, Yi Zhang +4

Lane detection is one of the most important tasks in self-driving. Due to various complex scenarios (e.g., severe occlusion, ambiguous lanes, etc.) and the sparse supervisory signa…