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Hang Xu

19 papers here

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

author position
  • first author1
  • middle author15
  • last author3

Across the 19 of 19 papers where every author was matched, so the position is known.

fields
  • cs.CV16
  • cs.AI1
  • cs.LG1
  • nlin.PS1
ORCID 0000-0003-4176-0738
same name
  • Hang Xu — 27 papers
  • Hang Xu — 17 papers
  • Hang Xu — 2 papers, h 6
  • Hang Xu — 2 papers
  • Hang Xu — 2 papers
  • Hang Xu — 2 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

activity
20212024
most citedViewCo: Discovering Text-Supervised Segmentation Masks via Multi-View Semantic Consistency

16 citations · 64 across the 19 of their papers we have counts for

collaborators

4 papers

cs.CV2022★ 11 cited

Exploring Visual Interpretability for Contrastive Language-Image Pre-training

Yi Li, Hualiang Wang, Yiqun Duan +2

Contrastive Language-Image Pre-training (CLIP) learns rich representations via readily available supervision of natural language. It improves the performance of downstream vision t…

cs.CV2022

RCLane: Relay Chain Prediction for Lane Detection

Shenghua Xu, Xinyue Cai, Bin Zhao +4

Lane detection is an important component of many real-world autonomous systems. Despite a wide variety of lane detection approaches have been proposed, reporting steady benchmark i…

cs.CV2022★ 1 cited

Open-world Semantic Segmentation via Contrasting and Clustering Vision-Language Embedding

Quande Liu, Youpeng Wen, Jianhua Han +3

To bridge the gap between supervised semantic segmentation and real-world applications that acquires one model to recognize arbitrary new concepts, recent zero-shot segmentation at…

nlin.PS2021★ 10 cited

Analysis of BBM solitary wave interactions using the conserved quantities

Xiangcheng You, Hang Xu, Qiang Sun

In this paper, a simple, robust, fast and effective method based on the conserved quantities is developed to approximate and analyse the shape, structure and interaction characters…

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