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Jie Huang

1 paper here

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

author position
  • middle author1

Across the 1 of 1 paper where every author was matched, so the position is known.

fields
  • cs.CV1
ORCID 0000-0002-3518-3404
same name
  • Jie Huang — 12 papers
  • Jie Huang — 8 papers, h 25
  • Jie Huang — 6 papers
  • Jie Huang — 5 papers, h 65
  • Jie Huang — 5 papers
  • Jie Huang — 5 papers, h 33

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 citedEnhancement by Your Aesthetic: An Intelligible Unsupervised Personalized Enhancer for Low-Light Images

12 citations · 12 across the 1 of their papers we have counts for

collaborators

4 papers

cs.CL2024

SimulBench: Evaluating Language Models with Creative Simulation Tasks

Qi Jia, Xiang Yue, Tianyu Zheng +2

We introduce SimulBench, a benchmark designed to evaluate large language models (LLMs) across a diverse collection of creative simulation scenarios, such as acting as a Linux termi…

cs.CV2024

Segmentation by registration-enabled SAM prompt engineering using five reference images

Yaxi Chen, Aleksandra Ivanova, Shaheer U. Saeed +4

The recently proposed Segment Anything Model (SAM) is a general tool for image segmentation, but it requires additional adaptation and careful fine-tuning for medical image segment…

cs.CV2023★ 2 cited

Generalized Lightness Adaptation with Channel Selective Normalization

Mingde Yao, Jie Huang, Xin Jin +4

Lightness adaptation is vital to the success of image processing to avoid unexpected visual deterioration, which covers multiple aspects, e.g., low-light image enhancement, image r…

cs.CV2022★ 12 cited

Enhancement by Your Aesthetic: An Intelligible Unsupervised Personalized Enhancer for Low-Light Images

Naishan Zheng, Jie Huang, Qi Zhu +3

Low-light image enhancement is an inherently subjective process whose targets vary with the user's aesthetic. Motivated by this, several personalized enhancement methods have been…

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