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researcher

Jie Li

27 papers hereh-index 507.6k citations206 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author24
  • last author3

Across the 27 of 27 papers where every author was matched, so the position is known.

fields
  • cs.CV25
  • eess.IV2
same name
  • Jie Li — 41 papers, h 46
  • Jie Li — 39 papers, h 35
  • Jie Li — 39 papers, h 21
  • Jie Li — 37 papers, h 14
  • Jie Li — 21 papers, h 16
  • Jie Li — 19 papers, h 14

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20152024
most citedAn Attention-Guided Deep Regression Model for Landmark Detection in Cephalograms

114 citations · 373 across the 27 of their papers we have counts for

collaborators
Showing 2020Show all

4 papers · 1 filter

cs.CV2020

Multi-Margin based Decorrelation Learning for Heterogeneous Face Recognition

Bing Cao, Nannan Wang, Xinbo Gao +2

Heterogeneous face recognition (HFR) refers to matching face images acquired from different domains with wide applications in security scenarios. This paper presents a deep neural…

cs.CV2020★ 1 cited

Facial Attribute Capsules for Noise Face Super Resolution

Jingwei Xin, Nannan Wang, Xinrui Jiang +3

Existing face super-resolution (SR) methods mainly assume the input image to be noise-free. Their performance degrades drastically when applied to real-world scenarios where the in…

cs.CV2020★ 1 cited

Video Face Super-Resolution with Motion-Adaptive Feedback Cell

Jingwei Xin, Nannan Wang, Jie Li +2

Video super-resolution (VSR) methods have recently achieved a remarkable success due to the development of deep convolutional neural networks (CNN). Current state-of-the-art CNN me…

cs.CV2020★ 39 cited

Image Fine-grained Inpainting

Zheng Hui, Jie Li, Xiumei Wang +1

Image inpainting techniques have shown promising improvement with the assistance of generative adversarial networks (GANs) recently. However, most of them often suffered from compl…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.