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researcher

Jun Ma

4 papers hereh-index 225 citations8 works total

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

author position
  • first author2
  • middle author2

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

fields
  • cs.CV3
  • cs.GR1
same name
  • Jun Ma — 132 papers, h 26
  • Jun Ma — 30 papers, h 11
  • Jun Ma — 26 papers, h 21
  • Jun Ma — 25 papers, h 10
  • Jun Ma — 19 papers
  • Jun Ma — 16 papers, h 23

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
20242026
most citedTimeline and Boundary Guided Diffusion Network for Video Shadow Detection

15 citations · 15 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CV2026

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation

Junyuan Ma, Xunzhi Xiang, Wenbin Li +2

Vision foundation models (VFMs) have achieved strong performance across various vision tasks. However, it still remains challenging to apply VFMs for cross-domain few-shot segmenta…

cs.CV2026

Toward Real-World High-Precision Image Matting and Segmentation

Haipeng Zhou, Zhaohu Xing, Hongqiu Wang +3

High-precision scene parsing tasks, including image matting and dichotomous segmentation, aim to accurately predict masks with extremely fine details (such as hair). Most existing…

cs.GR2025

One-shot Embroidery Customization via Contrastive LoRA Modulation

Jun Ma, Qian He, Gaofeng He +4

Diffusion models have significantly advanced image manipulation techniques, and their ability to generate photorealistic images is beginning to transform retail workflows, particul…

cs.CV2024★ 15 cited

Timeline and Boundary Guided Diffusion Network for Video Shadow Detection

Haipeng Zhou, Honqiu Wang, Tian Ye +5

Video Shadow Detection (VSD) aims to detect the shadow masks with frame sequence. Existing works suffer from inefficient temporal learning. Moreover, few works address the VSD prob…

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