activity
20222024
most citedTowards Efficient Adversarial Training on Vision Transformers

2 citations · 6 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV20241 cited

Follow-Your-Click: Open-domain Regional Image Animation via Short Prompts

Yue Ma, Yingqing He, Hongfa Wang +8

Despite recent advances in image-to-video generation, better controllability and local animation are less explored. Most existing image-to-video methods are not locally aware and t…

cs.CV20232 cited

Img2Vec: A Teacher of High Token-Diversity Helps Masked AutoEncoders

Heng Pan, Chenyang Liu, Wenxiao Wang +4

We present a pipeline of Image to Vector (Img2Vec) for masked image modeling (MIM) with deep features. To study which type of deep features is appropriate for MIM as a learning tar…

cs.CV2022

Towards In-distribution Compatibility in Out-of-distribution Detection

Boxi Wu, Jie Jiang, Haidong Ren +7

Deep neural network, despite its remarkable capability of discriminating targeted in-distribution samples, shows poor performance on detecting anomalous out-of-distribution data. T…

cs.CV20221 cited

Hardly Perceptible Trojan Attack against Neural Networks with Bit Flips

Jiawang Bai, Kuofeng Gao, Dihong Gong +3

The security of deep neural networks (DNNs) has attracted increasing attention due to their widespread use in various applications. Recently, the deployed DNNs have been demonstrat…

cs.CR2022

Versatile Weight Attack via Flipping Limited Bits

Jiawang Bai, Baoyuan Wu, Zhifeng Li +1

To explore the vulnerability of deep neural networks (DNNs), many attack paradigms have been well studied, such as the poisoning-based backdoor attack in the training stage and the…

cs.CV20222 cited

Towards Efficient Adversarial Training on Vision Transformers

Boxi Wu, Jindong Gu, Zhifeng Li +3

Vision Transformer (ViT), as a powerful alternative to Convolutional Neural Network (CNN), has received much attention. Recent work showed that ViTs are also vulnerable to adversar…