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Yuming Fang

4 papers here

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

author position
  • middle author4

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

fields
  • cs.CV4
ORCID 0000-0002-6946-3586
same name
  • Yuming Fang — 23 papers, h 53
  • Yuming Fang — 5 papers, h 7
  • Yuming Fang — 5 papers, h 6
  • Yuming Fang — 4 papers, h 6
  • Yuming Fang — 4 papers
  • 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 citedPerceptual Quality Assessment of 360∘ Images Based on Generative Scanpath Representation

1 citations · 1 across the 4 of their papers we have counts for

collaborators

4 papers

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…

cs.CV2024

2AFC Prompting of Large Multimodal Models for Image Quality Assessment

Hanwei Zhu, Xiangjie Sui, Baoliang Chen +4

While abundant research has been conducted on improving high-level visual understanding and reasoning capabilities of large multimodal models~(LMMs), their visual quality assessmen…

cs.CV2023★ 1 cited

Perceptual Quality Assessment of 360∘ Images Based on Generative Scanpath Representation

Xiangjie Sui, Hanwei Zhu, Xuelin Liu +3

Despite substantial efforts dedicated to the design of heuristic models for omnidirectional (i.e., 360∘) image quality assessment (OIQA), a conspicuous gap remains due to th…

cs.CV2023

Harmonizing Base and Novel Classes: A Class-Contrastive Approach for Generalized Few-Shot Segmentation

Weide Liu, Zhonghua Wu, Yang Zhao +4

Current methods for few-shot segmentation (FSSeg) have mainly focused on improving the performance of novel classes while neglecting the performance of base classes. To overcome th…

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