5 citations · 6 across the 7 of their papers we have counts for
8 papers
Composable Visual Tokenizers with Generator-Free Diagnostics of Learnability
Bingchen Zhao, Qiushan Guo, Ye Wang +3
We introduce CompTok, a training framework for learning visual tokenizers whose tokens are enhanced for compositionality. CompTok uses a token-conditioned diffusion decoder. By emp…
DNA: Uncovering Universal Latent Forgery Knowledge
Jingtong Dou, Chuancheng Shi, Yemin Wang +6
As generative AI achieves hyper-realism, superficial artifact detection has become obsolete. While prevailing methods rely on resource-intensive fine-tuning of black-box backbones,…
PairHuman: A High-Fidelity Photographic Dataset for Customized Dual-Person Generation
Ting Pan, Ye Wang, Peiguang Jing +3
Personalized dual-person portrait customization has considerable potential applications, such as preserving emotional memories and facilitating wedding photography planning. Howeve…
FreeControl: Efficient, Training-Free Structural Control via One-Step Attention Extraction
Jiang Lin, Xinyu Chen, Song Wu +7
Controlling the spatial and semantic structure of diffusion-generated images remains a challenge. Existing methods like ControlNet rely on handcrafted condition maps and retraining…
An Overview of the JPEG AI Learning-Based Image Coding Standard
Semih Esenlik, Yaojun Wu, Zhaobin Zhang +5
JPEG AI is an emerging learning-based image coding standard developed by Joint Photographic Experts Group (JPEG). The scope of the JPEG AI is the creation of a practical learning-b…
DP-Adapter: Dual-Pathway Adapter for Boosting Fidelity and Text Consistency in Customizable Human Image Generation
Ye Wang, Xuping Xie, Lanjun Wang +2
With the growing popularity of personalized human content creation and sharing, there is a rising demand for advanced techniques in customized human image generation. However, curr…
SigStyle: Signature Style Transfer via Personalized Text-to-Image Models
Ye Wang, Tongyuan Bai, Xuping Xie +3
Style transfer enables the seamless integration of artistic styles from a style image into a content image, resulting in visually striking and aesthetically enriched outputs. Despi…
Consistent Diffusion: Denoising Diffusion Model with Data-Consistent Training for Image Restoration
Xinlong Cheng, Tiantian Cao, Guoan Cheng +7
In this work, we address the limitations of denoising diffusion models (DDMs) in image restoration tasks, particularly the shape and color distortions that can compromise image qua…