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20182021
most citedResidual Non-local Attention Networks for Image Restoration

203 citations · 204 across the 2 of their papers we have counts for

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5 papers · 1 filter

cs.CV20211 cited

RPCL: A Framework for Improving Cross-Domain Detection with Auxiliary Tasks

Kai Li, Curtis Wigington, Chris Tensmeyer +5

Cross-Domain Detection (XDD) aims to train an object detector using labeled image from a source domain but have good performance in the target domain with only unlabeled images. Ex…

cs.CV2021

ECACL: A Holistic Framework for Semi-Supervised Domain Adaptation

Kai Li, Chang Liu, Handong Zhao +2

This paper studies Semi-Supervised Domain Adaptation (SSDA), a practical yet under-investigated research topic that aims to learn a model of good performance using unlabeled sample…

cs.CV2019203 cited

Residual Non-local Attention Networks for Image Restoration

Yulun Zhang, Kunpeng Li, Kai Li +2

In this paper, we propose a residual non-local attention network for high-quality image restoration. Without considering the uneven distribution of information in the corrupted ima…

cs.CV2018

Support Neighbor Loss for Person Re-Identification

Kai Li, Zhengming Ding, Kunpeng Li +2

Person re-identification (re-ID) has recently been tremendously boosted due to the advancement of deep convolutional neural networks (CNN). The majority of deep re-ID methods focus…

cs.CV2018

Image Super-Resolution Using Very Deep Residual Channel Attention Networks

Yulun Zhang, Kunpeng Li, Kai Li +3

Convolutional neural network (CNN) depth is of crucial importance for image super-resolution (SR). However, we observe that deeper networks for image SR are more difficult to train…