102 citations · 315 across the 17 of their papers we have counts for
6 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…
PointCAT: Contrastive Adversarial Training for Robust Point Cloud Recognition
Qidong Huang, Xiaoyi Dong, Dongdong Chen +5
Notwithstanding the prominent performance achieved in various applications, point cloud recognition models have often suffered from natural corruptions and adversarial perturbation…
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)…
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…
Shape-invariant 3D Adversarial Point Clouds
Qidong Huang, Xiaoyi Dong, Dongdong Chen +3
Adversary and invisibility are two fundamental but conflict characters of adversarial perturbations. Previous adversarial attacks on 3D point cloud recognition have often been crit…