Demo Data from SIGIR 2026 / reviewers in the wild / expert
Gwangjin Ju
dblp:297/4107
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2022
0000-0003-1006-8351ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-author · 2 since 2021Artificial intelligence and machine learning · 1 · 1 first-author · 1 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.
| Computer graphics and multimedia
1 paper |
Visual content generation and editing · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Visual content generation and editing › image generation › stylized image generation
caricature generation |
0.5 | 1 | 2021 | StyleCariGAN: caricature generation via StyleGAN feature map modulation · ACM Trans. Graph. 2021 |
Methods — techniques the papers use, named apart from their topics
layer mixing · 0.5StyleGAN · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Self-Supervised Dehazing Network Using Physical Priors
Gwangjin Ju, Yeongcheol Choi, Jee Hyun Paik, Gyeongha Hwang, Seungyong Lee 0001 |
ACCV (3) | 1 |
| 2021 | StyleCariGAN: caricature generation via StyleGAN feature map modulationabstractWe present a caricature generation framework based on shape and style manipulation using StyleGAN. Our framework, dubbed StyleCariGAN , automatically creates a realistic and detailed caricature from an input photo with optional controls on shape exaggeration degree and color stylization type. The key component of our method is shape exaggeration blocks that are used for modulating coarse layer feature maps of StyleGAN to produce desirable caricature shape exaggerations. We first build a layer-mixed StyleGAN for photo-to-caricature style conversion by swapping fine layers of the StyleGAN for photos to the corresponding layers of the StyleGAN trained to generate caricatures. Given an input photo, the layer-mixed model produces detailed color stylization for a caricature but without shape exaggerations. We then append shape exaggeration blocks to the coarse layers of the layer-mixed model and train the blocks to create shape exaggerations while preserving the characteristic appearances of the input. Experimental results show that our StyleCariGAN generates realistic and detailed caricatures compared to the current state-of-the-art methods. We demonstrate StyleCariGAN also supports other StyleGAN-based image manipulations, such as facial expression control. Wonjong Jang, Gwangjin Ju, Yucheol Jung, Jiaolong Yang, Xin Tong 0001, Seungyong Lee 0001 |
ACM Trans. Graph. | 2 |