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Bin Dong

5 papers here

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

fields
  • cs.CV4
  • cs.LG1
ORCID 0000-0002-0349-446X
same name
  • Bin Dong — 22 papers, h 23
  • Bin Dong — 4 papers
  • Bin Dong — 3 papers, h 21
  • Bin Dong — 3 papers
  • Bin Dong — 2 papers
  • Bin Dong — 2 papers

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 citedDiffusion Model for Generative Image Denoising

13 citations · 17 across the 5 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2023★ 2 cited

Unsupervised Image Denoising with Score Function

Yutong Xie, Mingze Yuan, Bin Dong +1

Though achieving excellent performance in some cases, current unsupervised learning methods for single image denoising usually have constraints in applications. In this paper, we p…

cs.CV2023★ 2 cited

Devil is in the Queries: Advancing Mask Transformers for Real-world Medical Image Segmentation and Out-of-Distribution Localization

Mingze Yuan, Yingda Xia, Hexin Dong +13

Real-world medical image segmentation has tremendous long-tailed complexity of objects, among which tail conditions correlate with relatively rare diseases and are clinically signi…

cs.CV2023★ 13 cited

Diffusion Model for Generative Image Denoising

Yutong Xie, Minne Yuan, Bin Dong +1

In supervised learning for image denoising, usually the paired clean images and noisy images are collected or synthesised to train a denoising model. L2 norm loss or other distance…

cs.CV2021

Implicit Feature Refinement for Instance Segmentation

Lufan Ma, Tiancai Wang, Bin Dong +3

We propose a novel implicit feature refinement module for high-quality instance segmentation. Existing image/video instance segmentation methods rely on explicitly stacked convolut…

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