155 citations · 280 across the 14 of their papers we have counts for
16 papers
Training for Identity, Inference for Controllability: A Unified Approach to Tuning-Free Face Personalization
Lianyu Pang, Ji Zhou, Qiping Wang +4
Tuning-free face personalization methods have developed along two distinct paradigms: text embedding approaches that map facial features into the text embedding space, and adapter-…
FreqEdit: Preserving High-Frequency Features for Robust Multi-Turn Image Editing
Yucheng Liao, Jiajun Liang, Kaiqian Cui +5
Instruction-based image editing through natural language has emerged as a powerful paradigm for intuitive visual manipulation. While recent models achieve impressive results on sin…
PairEdit: Learning Semantic Variations for Exemplar-based Image Editing
Haoguang Lu, Jiacheng Chen, Zhenguo Yang +4
Recent advancements in text-guided image editing have achieved notable success by leveraging natural language prompts for fine-grained semantic control. However, certain editing se…
ConsisLoRA: Enhancing Content and Style Consistency for LoRA-based Style Transfer
Bolin Chen, Baoquan Zhao, Haoran Xie +3
Style transfer involves transferring the style from a reference image to the content of a target image. Recent advancements in LoRA-based (Low-Rank Adaptation) methods have shown p…
CoRe: Context-Regularized Text Embedding Learning for Text-to-Image Personalization
Feize Wu, Yun Pang, Junyi Zhang +5
Recent advances in text-to-image personalization have enabled high-quality and controllable image synthesis for user-provided concepts. However, existing methods still struggle to…
AttnDreamBooth: Towards Text-Aligned Personalized Text-to-Image Generation
Lianyu Pang, Jian Yin, Baoquan Zhao +4
Recent advances in text-to-image models have enabled high-quality personalized image synthesis of user-provided concepts with flexible textual control. In this work, we analyze the…
Cross Initialization for Personalized Text-to-Image Generation
Lianyu Pang, Jian Yin, Haoran Xie +3
Recently, there has been a surge in face personalization techniques, benefiting from the advanced capabilities of pretrained text-to-image diffusion models. Among these, a notable…
Cycle Encoding of a StyleGAN Encoder for Improved Reconstruction and Editability
Xudong Mao, Liujuan Cao, Aurele T. Gnanha +3
GAN inversion aims to invert an input image into the latent space of a pre-trained GAN. Despite the recent advances in GAN inversion, there remain challenges to mitigate the tradeo…
Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme
Shaojie Li, Jie Wu, Xuefeng Xiao +3
Recently, a series of algorithms have been explored for GAN compression, which aims to reduce tremendous computational overhead and memory usages when deploying GANs on resource-co…
The ByteDance Speaker Diarization System for the VoxCeleb Speaker Recognition Challenge 2021
Keke Wang, Xudong Mao, Hao Wu +4
This paper describes the ByteDance speaker diarization system for the fourth track of the VoxCeleb Speaker Recognition Challenge 2021 (VoxSRC-21). The VoxSRC-21 provides both the d…
Image-to-image Translation via Hierarchical Style Disentanglement
Xinyang Li, Shengchuan Zhang, Jie Hu +6
Recently, image-to-image translation has made significant progress in achieving both multi-label (\ie, translation conditioned on different labels) and multi-style (\ie, generation…
Virtual Mixup Training for Unsupervised Domain Adaptation
Xudong Mao, Yun Ma, Zhenguo Yang +2
We study the problem of unsupervised domain adaptation which aims to adapt models trained on a labeled source domain to a completely unlabeled target domain. Recently, the cluster…
Unpaired Multi-Domain Image Generation via Regularized Conditional GANs
Xudong Mao, Qing Li
In this paper, we study the problem of multi-domain image generation, the goal of which is to generate pairs of corresponding images from different domains. With the recent develop…
On the Effectiveness of Least Squares Generative Adversarial Networks
Xudong Mao, Qing Li, Haoran Xie +3
Unsupervised learning with generative adversarial networks (GANs) has proven to be hugely successful. Regular GANs hypothesize the discriminator as a classifier with the sigmoid cr…
AlignGAN: Learning to Align Cross-Domain Images with Conditional Generative Adversarial Networks
Xudong Mao, Qing Li, Haoran Xie
Recently, several methods based on generative adversarial network (GAN) have been proposed for the task of aligning cross-domain images or learning a joint distribution of cross-do…
Least Squares Generative Adversarial Networks
Xudong Mao, Qing Li, Haoran Xie +3
Unsupervised learning with generative adversarial networks (GANs) has proven hugely successful. Regular GANs hypothesize the discriminator as a classifier with the sigmoid cross en…