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Xin Huang

4 papers here

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

author position
  • first author1
  • middle author3

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

fields
  • cs.CV4
same name
  • Xin Huang — 11 papers, h 23
  • Xin Huang — 11 papers
  • Xin Huang — 7 papers
  • Xin Huang — 7 papers
  • Xin Huang — 4 papers, h 4
  • Xin Huang — 4 papers

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

most citedNMS by Representative Region: Towards Crowded Pedestrian Detection by Proposal Pairing

31 citations · 40 across the 3 of their papers we have counts for

collaborators

4 papers

cs.CV2021★ 9 cited

LLA: Loss-aware Label Assignment for Dense Pedestrian Detection

Zheng Ge, Jianfeng Wang, Xin Huang +2

Label assignment has been widely studied in general object detection because of its great impact on detectors' performance. However, none of these works focus on label assignment i…

cs.CV2020

Delving into the Imbalance of Positive Proposals in Two-stage Object Detection

Zheng Ge, Zequn Jie, Xin Huang +2

Imbalance issue is a major yet unsolved bottleneck for the current object detection models. In this work, we observe two crucial yet never discussed imbalance issues. The first imb…

cs.CV2020★ 31 cited

NMS by Representative Region: Towards Crowded Pedestrian Detection by Proposal Pairing

Xin Huang, Zheng Ge, Zequn Jie +1

Although significant progress has been made in pedestrian detection recently, pedestrian detection in crowded scenes is still challenging. The heavy occlusion between pedestrians i…

cs.CV2020

PS-RCNN: Detecting Secondary Human Instances in a Crowd via Primary Object Suppression

Zheng Ge, Zequn Jie, Xin Huang +2

Detecting human bodies in highly crowded scenes is a challenging problem. Two main reasons result in such a problem: 1). weak visual cues of heavily occluded instances can hardly p…

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