66 citations · 231 across the 31 of their papers we have counts for
13 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…
X-Paste: Revisiting Scalable Copy-Paste for Instance Segmentation using CLIP and StableDiffusion
Hanqing Zhao, Dianmo Sheng, Jianmin Bao +9
Copy-Paste is a simple and effective data augmentation strategy for instance segmentation. By randomly pasting object instances onto new background images, it creates new training…
DeAR: A Deep-learning-based Audio Re-recording Resilient Watermarking
Chang Liu, Jie Zhang, Han Fang +3
Audio watermarking is widely used for leaking source tracing. The robustness of the watermark determines the traceability of the algorithm. With the development of digital technolo…
Ada3Diff: Defending against 3D Adversarial Point Clouds via Adaptive Diffusion
Kui Zhang, Hang Zhou, Jie Zhang +3
Deep 3D point cloud models are sensitive to adversarial attacks, which poses threats to safety-critical applications such as autonomous driving. Robust training and defend-by-denoi…
Cover Reproducible Steganography via Deep Generative Models
Kejiang Chen, Hang Zhou, Yaofei Wang +3
Whereas cryptography easily arouses attacks by means of encrypting a secret message into a suspicious form, steganography is advantageous for its resilience to attacks by concealin…
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)…
Go Wide or Go Deep: Levering Watermarking Performance with Computational Cost for Specific Images
Zhaoyang Jia, Han Fang, Zehua Ma +1
Digital watermarking has been widely studied for the protection of intellectual property. Traditional watermarking schemes often design in a "wider" rule, which applies one general…
Invertible Mask Network for Face Privacy-Preserving
Yang Yang, Yiyang Huang, Ming Shi +3
Face privacy-preserving is one of the hotspots that arises dramatic interests of research. However, the existing face privacy-preserving methods aim at causing the missing of seman…
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…
Self-supervised Transformer for Deepfake Detection
Hanqing Zhao, Wenbo Zhou, Dongdong Chen +2
The fast evolution and widespread of deepfake techniques in real-world scenarios require stronger generalization abilities of face forgery detectors. Some works capture the feature…