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Chen Li

5 papers hereh-index 466 citations6 works total

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

author position
  • first author3
  • middle author2

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • eess.IV1
same name
  • Chen Li — 34 papers, h 34
  • Chen Li — 23 papers, h 15
  • Chen Li — 17 papers, h 6
  • Chen Li — 13 papers, h 3
  • Chen Li — 13 papers, h 3
  • Chen Li — 13 papers, h 1

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
20232025
most citedCalibrating Uncertainty for Semi-Supervised Crowd Counting

2 citations · 2 across the 4 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2025

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation

Meilong Xu, Xiaoling Hu, Shahira Abousamra +2

In semi-supervised segmentation, capturing meaningful semantic structures from unlabeled data is essential. This is particularly challenging in histopathology image analysis, where…

cs.CV2025

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA

Chen Li, Meilong Xu, Xiaoling Hu +2

Robust medical image segmentation across modalities remains challenging due to severe domain shifts and the lack of target-domain labels. While diffusion models have been explored…

cs.CV2024

Spatial Diffusion for Cell Layout Generation

Chen Li, Xiaoling Hu, Shahira Abousamra +2

Generative models, such as GANs and diffusion models, have been used to augment training sets and boost performances in different tasks. We focus on generative models for cell dete…

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…

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