activity
20182026
most citedConvolutional Transformer based Dual Discriminator Generative Adversarial Networks for Video Anomaly Detection

96 citations · 165 across the 22 of their papers we have counts for

collaborators
Showing 2021Show all

6 papers · 1 filter

cs.CL2021

Do Multi-Lingual Pre-trained Language Models Reveal Consistent Token Attributions in Different Languages?

Junxiang Wang, Xuchao Zhang, Bo Zong +5

During the past several years, a surge of multi-lingual Pre-trained Language Models (PLMs) has been proposed to achieve state-of-the-art performance in many cross-lingual downstrea…

cs.CL2021★ 2 cited

Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-sentence Dependency Graph

Liyan Xu, Xuchao Zhang, Bo Zong +6

We target the task of cross-lingual Machine Reading Comprehension (MRC) in the direct zero-shot setting, by incorporating syntactic features from Universal Dependencies (UD), and t…

cs.CV2021★ 96 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.CL2021★ 3 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…