activity
20192022
most citedPretraining is All You Need for Image-to-Image Translation

92 citations · 144 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CV20229 cited

Paint by Example: Exemplar-based Image Editing with Diffusion Models

Binxin Yang, Shuyang Gu, Bo Zhang +5

Language-guided image editing has achieved great success recently. In this paper, for the first time, we investigate exemplar-guided image editing for more precise control. We achi…

cs.CV2022

3DFaceShop: Explicitly Controllable 3D-Aware Portrait Generation

Junshu Tang, Bo Zhang, Binxin Yang +4

In contrast to the traditional avatar creation pipeline which is a costly process, contemporary generative approaches directly learn the data distribution from photographs. While p…

cs.CV202292 cited

Pretraining is All You Need for Image-to-Image Translation

Tengfei Wang, Ting Zhang, Bo Zhang +4

We propose to use pretraining to boost general image-to-image translation. Prior image-to-image translation methods usually need dedicated architectural design and train individual…

cs.CV20221 cited

Protecting Celebrities from DeepFake with Identity Consistency Transformer

Xiaoyi Dong, Jianmin Bao, Dongdong Chen +6

In this work we propose Identity Consistency Transformer, a novel face forgery detection method that focuses on high-level semantics, specifically identity information, and detecti…

cs.CV20221 cited

Semi-Supervised Image-to-Image Translation using Latent Space Mapping

Pan Zhang, Jianmin Bao, Ting Zhang +2

Recent image-to-image translation works have been transferred from supervised to unsupervised settings due to the expensive cost of capturing or labeling large amounts of paired da…

cs.CV202136 cited

Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation

Pan Zhang, Bo Zhang, Ting Zhang +3

Self-training is a competitive approach in domain adaptive segmentation, which trains the network with the pseudo labels on the target domain. However inevitably, the pseudo labels…