Demo Data from EDBT 2026 / reviewers in the wild / expert
Shin-I Cheng
dblp:325/4321
· DBLP profile ↗
3ranked-venue papers
1as first author
3since 2021 · last 2024
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 3 · 1 first-author · 3 since 2021Artificial intelligence and machine learning · 2 · 2 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
2 papers |
Generative modeling · 69% Representation and self-supervised learning · 31% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Generative modeling
image generation |
0.8 | 1 | 2024 | Label-Free Neural Semantic Image Synthesis · ECCV (53) 2024 |
Machine learning › Generative modeling › image generation › conditional image synthesis
semantic image synthesis |
0.8 | 1 | 2024 | Label-Free Neural Semantic Image Synthesis · ECCV (53) 2024 |
Machine learning › Generative modeling › generative adversarial network
image-to-image translation |
0.6 | 1 | 2022 | Vector Quantized Image-to-Image Translation · ECCV (16) 2022 |
Machine learning › Representation and self-supervised learning
vector quantization |
0.2 | 1 | 2022 | Vector Quantized Image-to-Image Translation · ECCV (16) 2022 |
Methods — techniques the papers use, named apart from their topics
vector quantization · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Label-Free Neural Semantic Image Synthesis
Kevin Alexander Laube, Jan Hendrik Metzen, Shin-I Cheng, Julio Borges, Anna Khoreva |
ECCV (53) | 5 |
| 2023 | Adaptively-Realistic Image Generation from Stroke and Sketch with Diffusion ModelabstractGenerating images from hand-drawings is a crucial and fundamental task in content creation. The translation is difficult as there exist infinite possibilities and the different users usually expect different outcomes. Therefore, we propose a unified framework supporting a three-dimensional control over the image synthesis from sketches and strokes based on diffusion models. Users can not only decide the level of faithfulness to the input strokes and sketches, but also the degree of realism, as the user inputs are usually not consistent with the real images. Qualitative and quantitative experiments demonstrate that our framework achieves state-of-the-art performance while providing flexibility in generating customized images with control over shape, color, and realism. Moreover, our method unleashes applications such as editing on real images, generation with partial sketches and strokes, and multi-domain multi-modal synthesis. Shin-I Cheng, Yu-Jie Chen, Walon Wei-Chen Chiu, Hung-Yu Tseng, Hsin-Ying Lee 0001 |
WACV | 1 |
| 2022 | Vector Quantized Image-to-Image Translation
Yu-Jie Chen, Shin-I Cheng, Walon Wei-Chen Chiu, Hung-Yu Tseng, Hsin-Ying Lee 0001 |
ECCV (16) | 2 |