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researcher

Xin Huang

5 papers here

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

author position
  • first author4
  • middle author1

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.MM4
  • cs.LG1
ORCID 0000-0001-7638-4280
same name
  • Xin Huang — 17 papers, h 13
  • Xin Huang — 12 papers, h 23
  • Xin Huang — 12 papers, h 4
  • Xin Huang — 11 papers, h 10
  • Xin Huang — 10 papers, h 7
  • Xin Huang — 9 papers, h 11

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 citedCross-modal Common Representation Learning by Hybrid Transfer Network

14 citations · 32 across the 5 of their papers we have counts for

collaborators
Showing cs.MMShow all

4 papers · 1 filter

cs.MM2018★ 3 cited

Deep Cross-media Knowledge Transfer

Xin Huang, Yuxin Peng

Cross-media retrieval is a research hotspot in multimedia area, which aims to perform retrieval across different media types such as image and text. The performance of existing met…

cs.MM2017★ 11 cited

MHTN: Modal-adversarial Hybrid Transfer Network for Cross-modal Retrieval

Xin Huang, Yuxin Peng, Mingkuan Yuan

Cross-modal retrieval has drawn wide interest for retrieval across different modalities of data. However, existing methods based on DNN face the challenge of insufficient cross-mod…

cs.MM2017★ 14 cited

Cross-modal Common Representation Learning by Hybrid Transfer Network

Xin Huang, Yuxin Peng, Mingkuan Yuan

DNN-based cross-modal retrieval is a research hotspot to retrieve across different modalities as image and text, but existing methods often face the challenge of insufficient cross…

cs.MM2017★ 1 cited

Cross-media Similarity Metric Learning with Unified Deep Networks

Jinwei Qi, Xin Huang, Yuxin Peng

As a highlighting research topic in the multimedia area, cross-media retrieval aims to capture the complex correlations among multiple media types. Learning better shared represent…

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