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R. Ji

40 papers hereh-index 6518.8k citations318 works total

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

author position
  • middle author24
  • last author16

Across the 40 of 40 papers where every author was matched, so the position is known.

fields
  • cs.CV31
  • cs.IR3
  • cs.LG3
  • cs.CL1
  • cs.MM1
  • eess.IV1
same name
  • R. Ji — 5 papers, h 3
  • R. Ji — 2 papers, h 26
  • R. Ji — 1 paper, h 2
  • R. Ji — 1 paper, h 0

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
20152025
most citedTowards Optimal Structured CNN Pruning via Generative Adversarial Learning

32 citations · 171 across the 24 of their papers we have counts for

collaborators
Showing 2021 · cs.CVShow all

4 papers · 2 filters

cs.CV2021★ 31 cited

Long-Range Feature Propagating for Natural Image Matting

Qinglin Liu, Haozhe Xie, Shengping Zhang +2

Natural image matting estimates the alpha values of unknown regions in the trimap. Recently, deep learning based methods propagate the alpha values from the known regions to unknow…

cs.CV2021★ 8 cited

HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping

Yuhan Wang, Xu Chen, Junwei Zhu +7

In this work, we propose a high fidelity face swapping method, called HifiFace, which can well preserve the face shape of the source face and generate photo-realistic results. Unli…

cs.CV2021

Local Relation Learning for Face Forgery Detection

Shen Chen, Taiping Yao, Yang Chen +3

With the rapid development of facial manipulation techniques, face forgery detection has received considerable attention in digital media forensics due to security concerns. Most e…

cs.CV2021

Carrying out CNN Channel Pruning in a White Box

Yuxin Zhang, Mingbao Lin, Chia-Wen Lin +5

Channel Pruning has been long studied to compress CNNs, which significantly reduces the overall computation. Prior works implement channel pruning in an unexplainable manner, which…

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