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Yong-Lu Li

40 papers hereh-index 181.6k citations51 works total

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

author position
  • first author7
  • middle author23
  • last author10

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

fields
  • cs.CV28
  • cs.RO9
  • cs.LG2
  • cs.AI1
same name
  • Yong-Lu Li — 14 papers, h 4
  • Yong-Lu Li — 9 papers, h 9
  • Yong-Lu Li — 7 papers, h 2
  • Yong-Lu Li — 2 papers, h 2
  • Yong-Lu 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
20202026
most citedTransferable Interactiveness Knowledge for Human-Object Interaction Detection

73 citations · 202 across the 39 of their papers we have counts for

collaborators
Showing 2020Show all

4 papers · 1 filter

cs.CV2020★ 64 cited

HOI Analysis: Integrating and Decomposing Human-Object Interaction

Yong-Lu Li, Xinpeng Liu, Xiaoqian Wu +2

Human-Object Interaction (HOI) consists of human, object and implicit interaction/verb. Different from previous methods that directly map pixels to HOI semantics, we propose a nove…

cs.CV2020★ 3 cited

Canonical Voting: Towards Robust Oriented Bounding Box Detection in 3D Scenes

Yang You, Zelin Ye, Yujing Lou +5

3D object detection has attracted much attention thanks to the advances in sensors and deep learning methods for point clouds. Current state-of-the-art methods like VoteNet regress…

cs.CV2020

UKPGAN: A General Self-Supervised Keypoint Detector

Yang You, Wenhai Liu, Yanjie Ze +3

Keypoint detection is an essential component for the object registration and alignment. In this work, we reckon keypoint detection as information compression, and force the model t…

cs.CV2020

DecAug: Augmenting HOI Detection via Decomposition

Yichen Xie, Hao-Shu Fang, Dian Shao +2

Human-object interaction (HOI) detection requires a large amount of annotated data. Current algorithms suffer from insufficient training samples and category imbalance within datas…

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