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Xiao Tan

36 papers hereh-index 322.4k citations59 works total

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

author position
  • middle author34
  • last author1

Across the 35 of 36 papers where every author was matched, so the position is known.

fields
  • cs.CV36
same name
  • Xiao Tan — 9 papers, h 3
  • Xiao Tan — 7 papers, h 4
  • Xiao Tan — 6 papers, h 11
  • Xiao Tan — 6 papers, h 4
  • Xiao Tan — 6 papers, h 7
  • Xiao Tan — 6 papers, h 3

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
20182023
most citedDiscriminative Sounding Objects Localization via Self-supervised Audiovisual Matching

68 citations · 285 across the 36 of their papers we have counts for

collaborators
Showing 2018Show all

4 papers · 1 filter

cs.CV2018

Semi-Supervised Learning for Face Sketch Synthesis in the Wild

Chaofeng Chen, Wei Liu, Xiao Tan +1

Face sketch synthesis has made great progress in the past few years. Recent methods based on deep neural networks are able to generate high quality sketches from face photos. Howev…

cs.CV2018★ 2 cited

Fine-grained Video Categorization with Redundancy Reduction Attention

Chen Zhu, Xiao Tan, Feng Zhou +4

For fine-grained categorization tasks, videos could serve as a better source than static images as videos have a higher chance of containing discriminative patterns. Nevertheless,…

cs.CV2018★ 1 cited

Improving Annotation for 3D Pose Dataset of Fine-Grained Object Categories

Yaming Wang, Xiao Tan, Yi Yang +4

Existing 3D pose datasets of object categories are limited to generic object types and lack of fine-grained information. In this work, we introduce a new large-scale dataset that c…

cs.CV2018★ 4 cited

3D Pose Estimation for Fine-Grained Object Categories

Yaming Wang, Xiao Tan, Yi Yang +4

Existing object pose estimation datasets are related to generic object types and there is so far no dataset for fine-grained object categories. In this work, we introduce a new lar…

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