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Xiaoling Hu

8 papers here

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

author position
  • sole author1
  • first author1
  • middle author5

Across the 7 of 8 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • cs.IT3
  • cs.LG1
ORCID 0000-0003-3188-3005
same name
  • Xiaoling Hu — 15 papers, h 14
  • Xiaoling Hu — 6 papers, h 18
  • Xiaoling Hu — 5 papers, h 3
  • Xiaoling Hu — 2 papers
  • Xiaoling Hu — 2 papers, h 3
  • Xiaoling Hu — 1 paper, 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
20222024
most citedConfidence Estimation Using Unlabeled Data

3 citations · 8 across the 8 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2024★ 1 cited

Learning Topological Representations for Deep Image Understanding

Xiaoling Hu

In many scenarios, especially biomedical applications, the correct delineation of complex fine-scaled structures such as neurons, tissues, and vessels is critical for downstream an…

cs.CV2023★ 2 cited

Calibrating Uncertainty for Semi-Supervised Crowd Counting

Chen Li, Xiaoling Hu, Shahira Abousamra +1

Semi-supervised crowd counting is an important yet challenging task. A popular approach is to iteratively generate pseudo-labels for unlabeled data and add them to the training set…

cs.CV2022

Deep Statistic Shape Model for Myocardium Segmentation

Xiaoling Hu, Xiao Chen, Yikang Liu +3

Accurate segmentation and motion estimation of myocardium have always been important in clinic field, which essentially contribute to the downstream diagnosis. However, existing me…

cs.CV2022★ 1 cited

Learning Topological Interactions for Multi-Class Medical Image Segmentation

Saumya Gupta, Xiaoling Hu, James Kaan +10

Deep learning methods have achieved impressive performance for multi-class medical image segmentation. However, they are limited in their ability to encode topological interactions…

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