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Haoshu Fang

41 papers hereh-index 349.2k citations61 works total

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

author position
  • first author11
  • middle author24
  • last author2

Across the 37 of 41 papers where every author was matched, so the position is known.

fields
  • cs.CV21
  • cs.RO20
same name
  • Haoshu Fang — 2 papers, h 2
  • Haoshu Fang — 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
20162026
most citedTransCG: A Large-Scale Real-World Dataset for Transparent Object Depth Completion and a Grasping Baseline

133 citations · 473 across the 32 of their papers we have counts for

collaborators
Showing 2020Show all

4 papers · 1 filter

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…

cs.CV2020

DIRV: Dense Interaction Region Voting for End-to-End Human-Object Interaction Detection

Hao-Shu Fang, Yichen Xie, Dian Shao +1

Recent years, human-object interaction (HOI) detection has achieved impressive advances. However, conventional two-stage methods are usually slow in inference. On the other hand, e…

cs.CV2020★ 11 cited

PaStaNet: Toward Human Activity Knowledge Engine

Yong-Lu Li, Liang Xu, Xinpeng Liu +7

Existing image-based activity understanding methods mainly adopt direct mapping, i.e. from image to activity concepts, which may encounter performance bottleneck since the huge gap…

cs.CV2020★ 5 cited

GraspNet: A Large-Scale Clustered and Densely Annotated Dataset for Object Grasping

Hao-Shu Fang, Chenxi Wang, Minghao Gou +1

Object grasping is critical for many applications, which is also a challenging computer vision problem. However, for the clustered scene, current researches suffer from the problem…

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