activity
20182022
most citedConvolutional Transformer based Dual Discriminator Generative Adversarial Networks for Video Anomaly Detection

96 citations · 103 across the 8 of their papers we have counts for

collaborators

9 papers

cs.LG2022

Deep Federated Anomaly Detection for Multivariate Time Series Data

Wei Zhu, Dongjin Song, Yuncong Chen +6

Despite the fact that many anomaly detection approaches have been developed for multivariate time series data, limited effort has been made on federated settings in which multivari…

cs.LG2022

Ordinal-Quadruplet: Retrieval of Missing Classes in Ordinal Time Series

Jurijs Nazarovs, Cristian Lumezanu, Qianying Ren +4

In this paper, we propose an ordered time series classification framework that is robust against missing classes in the training data, i.e., during testing we can prescribe classes…

cs.CV202196 cited

Convolutional Transformer based Dual Discriminator Generative Adversarial Networks for Video Anomaly Detection

Xinyang Feng, Dongjin Song, Yuncong Chen +3

Detecting abnormal activities in real-world surveillance videos is an important yet challenging task as the prior knowledge about video anomalies is usually limited or unavailable.…

cs.CV2021

FACESEC: A Fine-grained Robustness Evaluation Framework for Face Recognition Systems

Liang Tong, Zhengzhang Chen, Jingchao Ni +4

We present FACESEC, a framework for fine-grained robustness evaluation of face recognition systems. FACESEC evaluation is performed along four dimensions of adversarial modeling: t…

cs.CL20213 cited

Unsupervised Document Embedding via Contrastive Augmentation

Dongsheng Luo, Wei Cheng, Jingchao Ni +8

We present a contrasting learning approach with data augmentation techniques to learn document representations in an unsupervised manner. Inspired by recent contrastive self-superv…

cs.LG2021

Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series

Yinjun Wu, Jingchao Ni, Wei Cheng +7

Forecasting on sparse multivariate time series (MTS) aims to model the predictors of future values of time series given their incomplete past, which is important for many emerging…