activity
20192022
most citedLearning Transferable Self-attentive Representations for Action Recognition in Untrimmed Videos with Weak Supervision

5 citations · 7 across the 5 of their papers we have counts for

collaborators

8 papers

cs.SI20221 cited

Influence-aware Task Assignment in Spatial Crowdsourcing (Technical Report)

Xuanhao Chen, Yan Zhao, Kai Zheng +2

With the widespread diffusion of smartphones, Spatial Crowdsourcing (SC), which aims to assign spatial tasks to mobile workers, has drawn increasing attention in both academia and…

cs.LG2021

HIFI: Anomaly Detection for Multivariate Time Series with High-order Feature Interactions

Liwei Deng, Xuanhao Chen, Yan Zhao +1

Monitoring complex systems results in massive multivariate time series data, and anomaly detection of these data is very important to maintain the normal operation of the systems.…

cs.IR2021

Hierarchical Hyperedge Embedding-based Representation Learning for Group Recommendation

Lei Guo, Hongzhi Yin, Tong Chen +2

In this work, we study group recommendation in a particular scenario, namely Occasional Group Recommendation (OGR). Most existing works have addressed OGR by aggregating group memb…

cs.IR20201 cited

Overcoming Data Sparsity in Group Recommendation

Hongzhi Yin, Qinyong Wang, Kai Zheng +2

It has been an important task for recommender systems to suggest satisfying activities to a group of users in people's daily social life. The major challenge in this task is how to…

cs.DB2020

SOUP: Spatial-Temporal Demand Forecasting and Competitive Supply

Bolong Zheng, Qi Hu, Lingfeng Ming +4

We consider a setting with an evolving set of requests for transportation from an origin to a destination before a deadline and a set of agents capable of servicing the requests. I…

cs.CV2020

Reconstruction Regularized Deep Metric Learning for Multi-label Image Classification

Changsheng Li, Chong Liu, Lixin Duan +2

In this paper, we present a novel deep metric learning method to tackle the multi-label image classification problem. In order to better learn the correlations among images feature…