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Yaowei Wang

34 papers hereh-index 417.3k citations111 works total

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

author position
  • first author1
  • middle author28
  • last author1

Across the 30 of 34 papers where every author was matched, so the position is known.

fields
  • cs.CV29
  • cs.LG3
  • cs.CL1
  • cs.NE1
same name
  • Yaowei Wang — 20 papers, h 4
  • Yaowei Wang — 19 papers, h 5
  • Yaowei Wang — 19 papers, h 6
  • Yaowei Wang — 15 papers, h 4
  • Yaowei Wang — 12 papers, h 7
  • Yaowei Wang — 10 papers, h 4

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
20162024
most citedDeep Transfer Learning for Person Re-identification

219 citations · 831 across the 34 of their papers we have counts for

collaborators
Showing 2016Show all

4 papers · 1 filter

cs.CV2016★ 219 cited

Deep Transfer Learning for Person Re-identification

Mengyue Geng, Yaowei Wang, Tao Xiang +1

Person re-identification (Re-ID) poses a unique challenge to deep learning: how to learn a deep model with millions of parameters on a small training set of few or no labels. In th…

cs.CV2016★ 4 cited

Joint Network based Attention for Action Recognition

Yemin Shi, Yonghong Tian, Yaowei Wang +1

By extracting spatial and temporal characteristics in one network, the two-stream ConvNets can achieve the state-of-the-art performance in action recognition. However, such a frame…

cs.CV2016★ 5 cited

Learning long-term dependencies for action recognition with a biologically-inspired deep network

Yemin Shi, Yonghong Tian, Yaowei Wang +1

Despite a lot of research efforts devoted in recent years, how to efficiently learn long-term dependencies from sequences still remains a pretty challenging task. As one of the key…

cs.CV2016★ 209 cited

Sequential Deep Trajectory Descriptor for Action Recognition with Three-stream CNN

Yemin Shi, Yonghong Tian, Yaowei Wang +1

Learning the spatial-temporal representation of motion information is crucial to human action recognition. Nevertheless, most of the existing features or descriptors cannot capture…

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