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Xianming Liu

17 papers here

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

author position
  • first author1
  • middle author8
  • last author6

Across the 15 of 17 papers where every author was matched, so the position is known.

fields
  • cs.CV15
  • cs.CR1
  • eess.IV1
ORCID 0000-0002-8857-1785
same name
  • Xianming Liu — 23 papers, h 39
  • Xianming Liu — 10 papers, h 11
  • Xianming Liu — 10 papers, h 12
  • Xianming Liu — 4 papers, h 19
  • Xianming Liu — 2 papers
  • Xianming Liu — 2 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
20162024
most citedRandom Walk Graph Laplacian based Smoothness Prior for Soft Decoding of JPEG Images

128 citations · 167 across the 17 of their papers we have counts for

collaborators

4 papers

cs.CV2022

LiteDepth: Digging into Fast and Accurate Depth Estimation on Mobile Devices

Zhenyu Li, Zehui Chen, Jialei Xu +2

Monocular depth estimation is an essential task in the computer vision community. While tremendous successful methods have obtained excellent results, most of them are computationa…

cs.CV2021

Towards End-to-End Image Compression and Analysis with Transformers

Yuanchao Bai, Xu Yang, Xianming Liu +4

We propose an end-to-end image compression and analysis model with Transformers, targeting to the cloud-based image classification application. Instead of placing an existing Trans…

cs.CV2016★ 8 cited

Feedback Neural Network for Weakly Supervised Geo-Semantic Segmentation

Xianming Liu, Amy Zhang, Tobias Tiecke +2

Learning from weakly-supervised data is one of the main challenges in machine learning and computer vision, especially for tasks such as image semantic segmentation where labeling…

cs.CV2016★ 128 cited

Random Walk Graph Laplacian based Smoothness Prior for Soft Decoding of JPEG Images

Xianming Liu, Gene Cheung, Xiaolin Wu +1

Given the prevalence of JPEG compressed images, optimizing image reconstruction from the compressed format remains an important problem. Instead of simply reconstructing a pixel bl…

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