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Xiaobo Xia

34 papers hereh-index 232.8k citations52 works total

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

author position
  • first author9
  • middle author23

Across the 32 of 34 papers where every author was matched, so the position is known.

fields
  • cs.LG19
  • cs.CV10
  • cs.CL2
  • stat.ML2
  • cs.HC1
same name
  • Xiaobo Xia — 16 papers, h 6
  • Xiaobo Xia — 14 papers, h 7
  • Xiaobo Xia — 5 papers, h 3
  • Xiaobo Xia — 3 papers, h 1
  • Xiaobo Xia — 1 paper
  • Xiaobo Xia — 1 paper, 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
20192026
most citedAre Anchor Points Really Indispensable in Label-Noise Learning?

188 citations · 514 across the 33 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

cs.CV2021★ 15 cited

Objects in Semantic Topology

Shuo Yang, Peize Sun, Yi Jiang +6

A more realistic object detection paradigm, Open-World Object Detection, has arisen increasing research interests in the community recently. A qualified open-world object detector…

cs.LG2021

Kernel Mean Estimation by Marginalized Corrupted Distributions

Xiaobo Xia, Shuo Shan, Mingming Gong +4

Estimating the kernel mean in a reproducing kernel Hilbert space is a critical component in many kernel learning algorithms. Given a finite sample, the standard estimate of the tar…

cs.LG2021★ 7 cited

Instance Correction for Learning with Open-set Noisy Labels

Xiaobo Xia, Tongliang Liu, Bo Han +4

The problem of open-set noisy labels denotes that part of training data have a different label space that does not contain the true class. Lots of approaches, e.g., loss correction…

cs.LG2021★ 49 cited

Sample Selection with Uncertainty of Losses for Learning with Noisy Labels

Xiaobo Xia, Tongliang Liu, Bo Han +4

In learning with noisy labels, the sample selection approach is very popular, which regards small-loss data as correctly labeled during training. However, losses are generated on-t…

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