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Liang Zheng

4 papers hereh-index 349 citations6 works total

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

author position
  • last author4

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

fields
  • cs.CV4
same name
  • Liang Zheng — 52 papers, h 64
  • Liang Zheng — 15 papers
  • Liang Zheng — 14 papers, h 4
  • Liang Zheng — 11 papers, h 6
  • Liang Zheng — 10 papers, h 4
  • Liang Zheng — 9 papers, h 13

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

most citedPrivacy Assessment on Reconstructed Images: Are Existing Evaluation Metrics Faithful to Human Perception?

1 citations · 1 across the 2 of their papers we have counts for

collaborators

4 papers

cs.CV2024

Strong and Controllable Blind Image Decomposition

Zeyu Zhang, Junlin Han, Chenhui Gou +2

Blind image decomposition aims to decompose all components present in an image, typically used to restore a multi-degraded input image. While fully recovering the clean image is ap…

cs.CV2023★ 1 cited

Privacy Assessment on Reconstructed Images: Are Existing Evaluation Metrics Faithful to Human Perception?

Xiaoxiao Sun, Nidham Gazagnadou, Vivek Sharma +3

Hand-crafted image quality metrics, such as PSNR and SSIM, are commonly used to evaluate model privacy risk under reconstruction attacks. Under these metrics, reconstructed images…

cs.CV2023

Alice Benchmarks: Connecting Real World Re-Identification with the Synthetic

Xiaoxiao Sun, Yue Yao, Shengjin Wang +2

For object re-identification (re-ID), learning from synthetic data has become a promising strategy to cheaply acquire large-scale annotated datasets and effective models, with few…

cs.CV2023

CIFAR-10-Warehouse: Broad and More Realistic Testbeds in Model Generalization Analysis

Xiaoxiao Sun, Xingjian Leng, Zijian Wang +3

Analyzing model performance in various unseen environments is a critical research problem in the machine learning community. To study this problem, it is important to construct a t…

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