92 citations · 197 across the 18 of their papers we have counts for
9 papers · 1 filter
CLIP Itself is a Strong Fine-tuner: Achieving 85.7% and 88.0% Top-1 Accuracy with ViT-B and ViT-L on ImageNet
Xiaoyi Dong, Jianmin Bao, Ting Zhang +7
Recent studies have shown that CLIP has achieved remarkable success in performing zero-shot inference while its fine-tuning performance is not satisfactory. In this paper, we ident…
Rodin: A Generative Model for Sculpting 3D Digital Avatars Using Diffusion
Tengfei Wang, Bo Zhang, Ting Zhang +8
This paper presents a 3D generative model that uses diffusion models to automatically generate 3D digital avatars represented as neural radiance fields. A significant challenge in…
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…
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…
MaskCLIP: Masked Self-Distillation Advances Contrastive Language-Image Pretraining
Xiaoyi Dong, Jianmin Bao, Yinglin Zheng +9
This paper presents a simple yet effective framework MaskCLIP, which incorporates a newly proposed masked self-distillation into contrastive language-image pretraining. The core id…
Bootstrapped Masked Autoencoders for Vision BERT Pretraining
Xiaoyi Dong, Jianmin Bao, Ting Zhang +6
We propose bootstrapped masked autoencoders (BootMAE), a new approach for vision BERT pretraining. BootMAE improves the original masked autoencoders (MAE) with two core designs: 1)…