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Chia-Ming Chang

4 papers hereh-index 5226 citations18 works total

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

author position
  • first author1
  • middle author3

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

fields
  • cs.CV2
  • cs.GR1
  • cs.LG1
same name
  • Chia-Ming Chang — 3 papers, h 2
  • Chia-Ming Chang — 3 papers, h 3
  • Chia-Ming Chang — 2 papers, h 2
  • Chia-Ming Chang — 2 papers, h 8
  • Chia-Ming Chang — 1 paper, h 1
  • Chia-Ming Chang — 1 paper, h 3

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 citedDAMix: A Density-Aware Mixup Augmentation for Single Image Dehazing under Domain Shift

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

collaborators

4 papers

cs.LG2022

SpaceEditing: Integrating Human Knowledge into Deep Neural Networks via Interactive Latent Space Editing

Jiafu Wei, Ding Xia, Haoran Xie +3

We propose an interactive editing method that allows humans to help deep neural networks (DNNs) learn a latent space more consistent with human knowledge, thereby improving classif…

cs.CV2022

SGDraw: Scene Graph Drawing Interface Using Object-Oriented Representation

Tianyu Zhang, Xusheng Du, Chia-Ming Chang +2

Scene understanding is an essential and challenging task in computer vision. To provide the visually fundamental graphical structure of an image, the scene graph has received incre…

cs.GR2022

Sketch-based 3D Shape Modeling from Sparse Point Clouds

Xusheng Du, Yi He, Xi Yang +2

3D modeling based on point clouds is an efficient way to reconstruct and create detailed 3D content. However, the geometric procedure may lose accuracy due to high redundancy and t…

cs.CV2021★ 1 cited

DAMix: A Density-Aware Mixup Augmentation for Single Image Dehazing under Domain Shift

Chia-Ming Chang, Tsung-Nan Lin

Deep learning-based methods have achieved considerable success on single image dehazing in recent years. However, these methods are often subject to performance degradation when do…

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