most citedAdv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition

23 citations · 44 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG20228 cited

A K-variate Time Series Is Worth K Words: Evolution of the Vanilla Transformer Architecture for Long-term Multivariate Time Series Forecasting

Zanwei Zhou, Ruizhe Zhong, Chen Yang +3

Multivariate time series forecasting (MTSF) is a fundamental problem in numerous real-world applications. Recently, Transformer has become the de facto solution for MTSF, especiall…

cs.CV202223 cited

Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition

Shuai Jia, Bangjie Yin, Taiping Yao +4

Deep learning models have shown their vulnerability when dealing with adversarial attacks. Existing attacks almost perform on low-level instances, such as pixels and super-pixels,…

cs.LG20224 cited

DOTIN: Dropping Task-Irrelevant Nodes for GNNs

Shaofeng Zhang, Feng Zhu, Junchi Yan +2

Scalability is an important consideration for deep graph neural networks. Inspired by the conventional pooling layers in CNNs, many recent graph learning approaches have introduced…

cs.CV2022

Continual Predictive Learning from Videos

Geng Chen, Wendong Zhang, Han Lu +4

Predictive learning ideally builds the world model of physical processes in one or more given environments. Typical setups assume that we can collect data from all environments at…

cs.CV20225 cited

Exploring Frequency Adversarial Attacks for Face Forgery Detection

Shuai Jia, Chao Ma, Taiping Yao +3

Various facial manipulation techniques have drawn serious public concerns in morality, security, and privacy. Although existing face forgery classifiers achieve promising performan…

cs.CV20224 cited

EAutoDet: Efficient Architecture Search for Object Detection

Xiaoxing Wang, Jiale Lin, Junchi Yan +2

Training CNN for detection is time-consuming due to the large dataset and complex network modules, making it hard to search architectures on detection datasets directly, which usua…