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Yaoyi Li

4 papers hereh-index 460 citations6 works total

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

author position
  • first author2
  • middle author2

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV4
same name
  • Yaoyi Li — 15 papers, h 21
  • Yaoyi Li — 14 papers, h 4
  • Yaoyi Li — 5 papers, h 2
  • Yaoyi Li — 4 papers, h 6
  • Yaoyi Li — 1 paper, h 2

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
20162022
most citedInductive Guided Filter: Real-time Deep Image Matting with Weakly Annotated Masks on Mobile Devices

4 citations · 6 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CV2022★ 2 cited

Unsupervised Visual Representation Learning by Synchronous Momentum Grouping

Bo Pang, Yifan Zhang, Yaoyi Li +2

In this paper, we propose a genuine group-level contrastive visual representation learning method whose linear evaluation performance on ImageNet surpasses the vanilla supervised l…

cs.CV2021

Trimap-guided Feature Mining and Fusion Network for Natural Image Matting

Weihao Jiang, Dongdong Yu, Zhaozhi Xie +3

Utilizing trimap guidance and fusing multi-level features are two important issues for trimap-based matting with pixel-level prediction. To utilize trimap guidance, most existing a…

cs.CV2019★ 4 cited

Inductive Guided Filter: Real-time Deep Image Matting with Weakly Annotated Masks on Mobile Devices

Yaoyi Li, Jianfu Zhang, Weijie Zhao +1

Recently, significant progress has been achieved in deep image matting. Most of the classical image matting methods are time-consuming and require an ideal trimap which is difficul…

cs.CV2016

Multi-View Constraint Propagation with Consensus Prior Knowledge

Yaoyi Li, Hongtao Lu

In many applications, the pairwise constraint is a kind of weaker supervisory information which can be collected easily. The constraint propagation has been proved to be a success…

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