190 citations · 942 across the 77 of their papers we have counts for
19 papers · 1 filter
QuickVideo: Real-Time Long Video Understanding with System Algorithm Co-Design
Benjamin Schneider, Dongfu Jiang, Chao Du +2
Long-video understanding has emerged as a crucial capability in real-world applications such as video surveillance, meeting summarization, educational lecture analysis, and sports…
Error Analyses of Auto-Regressive Video Diffusion Models: A Unified Framework
Jing Wang, Fengzhuo Zhang, Xiaoli Li +5
Auto-Regressive Video Diffusion Models (AR-VDMs) have shown strong capabilities in generating long, photorealistic videos, but suffer from two key limitations: (i) history forgetti…
Real-time Identity Defenses against Malicious Personalization of Diffusion Models
Hanzhong Guo, Shen Nie, Chao Du +3
Personalized generative diffusion models, capable of synthesizing highly realistic images based on a few reference portraits, may pose substantial social, ethical, and legal risks…
Orient Anything: Learning Robust Object Orientation Estimation from Rendering 3D Models
Zehan Wang, Ziang Zhang, Tianyu Pang +3
Orientation is a key attribute of objects, crucial for understanding their spatial pose and arrangement in images. However, practical solutions for accurate orientation estimation…
Meta-Unlearning on Diffusion Models: Preventing Relearning Unlearned Concepts
Hongcheng Gao, Tianyu Pang, Chao Du +3
With the rapid progress of diffusion-based content generation, significant efforts are being made to unlearn harmful or copyrighted concepts from pretrained diffusion models (DMs)…
Improving Long-Text Alignment for Text-to-Image Diffusion Models
Luping Liu, Chao Du, Tianyu Pang +3
The rapid advancement of text-to-image (T2I) diffusion models has enabled them to generate unprecedented results from given texts. However, as text inputs become longer, existing e…
Revisiting Backdoor Attacks against Large Vision-Language Models from Domain Shift
Siyuan Liang, Jiawei Liang, Tianyu Pang +5
Instruction tuning enhances large vision-language models (LVLMs) but increases their vulnerability to backdoor attacks due to their open design. Unlike prior studies in static sett…
AdAM: Few-Shot Image Generation via Adaptation-Aware Kernel Modulation
Yunqing Zhao, Keshigeyan Chandrasegaran, Milad Abdollahzadeh +5
Few-shot image generation (FSIG) aims to learn to generate new and diverse images given few (e.g., 10) training samples. Recent work has addressed FSIG by leveraging a GAN pre-trai…
On Evaluating Adversarial Robustness of Large Vision-Language Models
Yunqing Zhao, Tianyu Pang, Chao Du +4
Large vision-language models (VLMs) such as GPT-4 have achieved unprecedented performance in response generation, especially with visual inputs, enabling more creative and adaptabl…
Exploring Incompatible Knowledge Transfer in Few-shot Image Generation
Yunqing Zhao, Chao Du, Milad Abdollahzadeh +4
Few-shot image generation (FSIG) learns to generate diverse and high-fidelity images from a target domain using a few (e.g., 10) reference samples. Existing FSIG methods select, pr…
A Recipe for Watermarking Diffusion Models
Yunqing Zhao, Tianyu Pang, Chao Du +3
Diffusion models (DMs) have demonstrated advantageous potential on generative tasks. Widespread interest exists in incorporating DMs into downstream applications, such as producing…
Better Diffusion Models Further Improve Adversarial Training
Zekai Wang, Tianyu Pang, Chao Du +3
It has been recognized that the data generated by the denoising diffusion probabilistic model (DDPM) improves adversarial training. After two years of rapid development in diffusio…
Controllable Evaluation and Generation of Physical Adversarial Patch on Face Recognition
Xiao Yang, Yinpeng Dong, Tianyu Pang +3
Recent studies have revealed the vulnerability of face recognition models against physical adversarial patches, which raises security concerns about the deployed face recognition s…
Unrestricted Adversarial Attacks on ImageNet Competition
Yuefeng Chen, Xiaofeng Mao, Yuan He +34
Many works have investigated the adversarial attacks or defenses under the settings where a bounded and imperceptible perturbation can be added to the input. However in the real-wo…
Adversarial Attacks on ML Defense Models Competition
Yinpeng Dong, Qi-An Fu, Xiao Yang +25
Due to the vulnerability of deep neural networks (DNNs) to adversarial examples, a large number of defense techniques have been proposed to alleviate this problem in recent years.…
Benchmarking Adversarial Robustness
Yinpeng Dong, Qi-An Fu, Xiao Yang +4
Deep neural networks are vulnerable to adversarial examples, which becomes one of the most important research problems in the development of deep learning. While a lot of efforts h…
Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks
Yinpeng Dong, Tianyu Pang, Hang Su +1
Deep neural networks are vulnerable to adversarial examples, which can mislead classifiers by adding imperceptible perturbations. An intriguing property of adversarial examples is…
Adversarial Attacks and Defences Competition
Alexey Kurakin, Ian Goodfellow, Samy Bengio +20
To accelerate research on adversarial examples and robustness of machine learning classifiers, Google Brain organized a NIPS 2017 competition that encouraged researchers to develop…
Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser
Fangzhou Liao, Ming Liang, Yinpeng Dong +3
Neural networks are vulnerable to adversarial examples, which poses a threat to their application in security sensitive systems. We propose high-level representation guided denoise…