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researcher

Xiaoyu Wang

4 papers here

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

author position
  • middle author4

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

fields
  • cs.CV3
  • cs.LG1
same name
  • Xiaoyu Wang — 29 papers, h 15
  • Xiaoyu Wang — 9 papers, h 20
  • Xiaoyu Wang — 4 papers
  • Xiaoyu Wang — 3 papers, h 12
  • Xiaoyu Wang — 3 papers, h 6
  • Xiaoyu Wang — 3 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
20202022
most citedTowards Semi-Supervised Deep Facial Expression Recognition with An Adaptive Confidence Margin

6 citations · 15 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CV2022★ 6 cited

Towards Semi-Supervised Deep Facial Expression Recognition with An Adaptive Confidence Margin

Hangyu Li, Nannan Wang, Xi Yang +2

Only parts of unlabeled data are selected to train models for most semi-supervised learning methods, whose confidence scores are usually higher than the pre-defined threshold (i.e.…

cs.CV2022★ 6 cited

Semi-parametric Makeup Transfer via Semantic-aware Correspondence

Mingrui Zhu, Yun Yi, Nannan Wang +2

The large discrepancy between the source non-makeup image and the reference makeup image is one of the key challenges in makeup transfer. Conventional approaches for makeup transfe…

cs.LG2021

Removing Adversarial Noise in Class Activation Feature Space

Dawei Zhou, Nannan Wang, Chunlei Peng +4

Deep neural networks (DNNs) are vulnerable to adversarial noise. Preprocessing based defenses could largely remove adversarial noise by processing inputs. However, they are typical…

cs.CV2020★ 3 cited

Weakly Supervised Temporal Action Localization with Segment-Level Labels

Xinpeng Ding, Nannan Wang, Xinbo Gao +3

Temporal action localization presents a trade-off between test performance and annotation-time cost. Fully supervised methods achieve good performance with time-consuming boundary…

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