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Jun Cheng

4 papers hereh-index 5127 citations12 works total

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
same name
  • Jun Cheng — 14 papers, h 32
  • Jun Cheng — 7 papers, h 15
  • Jun Cheng — 7 papers, h 8
  • Jun Cheng — 6 papers
  • Jun Cheng — 5 papers, h 2
  • Jun Cheng — 4 papers, h 2

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 citedGaussian Mixture based Evidential Learning for Stereo Matching

1 citations · 1 across the 3 of their papers we have counts for

collaborators

4 papers

cs.CV2026

Degradation-Aware Adaptive Context Gating for Unified Image Restoration

Lei He, Jielei Chu, Fengmao Lv +4

Unified image restoration using a single model often faces task interference due to diverse degradations. To address this, we propose DACG-IR (Degradation-Aware Adaptive Context Ga…

cs.CV2025

Modality-Aware Feature Matching in Visual and Vision-Language Applications: A Comprehensive Survey

Weide Liu, Wei Zhou, Jun Liu +4

Feature matching is a cornerstone task in computer vision, essential for applications such as image retrieval, stereo matching, 3D reconstruction, and SLAM. This survey comprehensi…

cs.CV2024★ 1 cited

Gaussian Mixture based Evidential Learning for Stereo Matching

Weide Liu, Xingxing Wang, Lu Wang +3

In this paper, we introduce a novel Gaussian mixture based evidential learning solution for robust stereo matching. Diverging from previous evidential deep learning approaches that…

cs.CV2024

Enhancing Incomplete Multi-modal Brain Tumor Segmentation with Intra-modal Asymmetry and Inter-modal Dependency

Weide Liu, Jingwen Hou, Xiaoyang Zhong +4

Deep learning-based brain tumor segmentation (BTS) models for multi-modal MRI images have seen significant advancements in recent years. However, a common problem in practice is th…

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