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Yuming Fang

6 papers hereh-index 679 citations28 works total

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

author position
  • middle author4
  • last author1

Across the 5 of 6 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • eess.IV2
same name
  • Yuming Fang — 31 papers, h 53
  • Yuming Fang — 6 papers, h 7
  • Yuming Fang — 5 papers, h 6
  • Yuming Fang — 5 papers, h 6
  • Yuming Fang — 4 papers, h 1
  • Yuming Fang — 3 papers, h 4

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 citedSubjective and Objective Quality Assessment of Non-Uniformly Distorted Omnidirectional Images

2 citations · 2 across the 5 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2025

Diffusion-based Facial Aesthetics Enhancement with 3D Structure Guidance

Lisha Li, Jingwen Hou, Weide Liu +2

Facial Aesthetics Enhancement (FAE) aims to improve facial attractiveness by adjusting the structure and appearance of a facial image while preserving its identity as much as possi…

cs.CV2025

Viewport-Unaware Blind Omnidirectional Image Quality Assessment: A Flexible and Effective Paradigm

Jiebin Yan, Kangcheng Wu, Junjie Chen +2

Most of existing blind omnidirectional image quality assessment (BOIQA) models rely on viewport generation by modeling user viewing behavior or transforming omnidirectional images…

cs.CV2025

Computational Analysis of Degradation Modeling in Blind Panoramic Image Quality Assessment

Jiebin Yan, Ziwen Tan, Jiale Rao +3

Blind panoramic image quality assessment (BPIQA) has recently brought new challenge to the visual quality community, due to the complex interaction between immersive content and hu…

cs.CV2024

Meta-Point Learning and Refining for Category-Agnostic Pose Estimation

Junjie Chen, Jiebin Yan, Yuming Fang +1

Category-agnostic pose estimation (CAPE) aims to predict keypoints for arbitrary classes given a few support images annotated with keypoints. Existing methods only rely on the feat…

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