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Shin-I Cheng

dblp:325/4321 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Machine learning › Generative modeling
image generation
0.812024
Label-Free Neural Semantic Image Synthesis · ECCV (53) 2024
Machine learning › Generative modeling › image generation › conditional image synthesis
semantic image synthesis
0.812024
Label-Free Neural Semantic Image Synthesis · ECCV (53) 2024
Machine learning › Generative modeling › generative adversarial network
image-to-image translation
0.612022
Vector Quantized Image-to-Image Translation · ECCV (16) 2022
Machine learning › Representation and self-supervised learning
vector quantization
0.212022
Vector Quantized Image-to-Image Translation · ECCV (16) 2022

Methods — techniques the papers use, named apart from their topics

vector quantization · 0.6
YearPublicationVenuePosition
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 Model
abstract
Generating 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
WACV1
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