443 citations · 544 across the 17 of their papers we have counts for
7 papers · 1 filter
MHCCL: Masked Hierarchical Cluster-Wise Contrastive Learning for Multivariate Time Series
Qianwen Meng, Hangwei Qian, Yong Liu +3
Learning semantic-rich representations from raw unlabeled time series data is critical for downstream tasks such as classification and forecasting. Contrastive learning has recentl…
Inductive Graph Transformer for Delivery Time Estimation
Xin Zhou, Jinglong Wang, Yong Liu +3
Providing accurate estimated time of package delivery on users' purchasing pages for e-commerce platforms is of great importance to their purchasing decisions and post-purchase exp…
TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
Haixu Wu, Tengge Hu, Yong Liu +3
Time series analysis is of immense importance in extensive applications, such as weather forecasting, anomaly detection, and action recognition. This paper focuses on temporal vari…
Revisiting Item Promotion in GNN-based Collaborative Filtering: A Masked Targeted Topological Attack Perspective
Yongwei Wang, Yong Liu, Zhiqi Shen
Graph neural networks (GNN) based collaborative filtering (CF) have attracted increasing attention in e-commerce and social media platforms. However, there still lack efforts to ev…
Learning Quality-aware Dynamic Memory for Video Object Segmentation
Yong Liu, Ran Yu, Fei Yin +4
Recently, several spatial-temporal memory-based methods have verified that storing intermediate frames and their masks as memory are helpful to segment target objects in videos. Ho…
UniInst: Unique Representation for End-to-End Instance Segmentation
Yimin Ou, Rui Yang, Lufan Ma +5
Existing instance segmentation methods have achieved impressive performance but still suffer from a common dilemma: redundant representations (e.g., multiple boxes, grids, and anch…
A Survey of Visual Sensory Anomaly Detection
Xi Jiang, Guoyang Xie, Jinbao Wang +4
Visual sensory anomaly detection (AD) is an essential problem in computer vision, which is gaining momentum recently thanks to the development of AI for good. Compared with semanti…