most citedIntent-aware Recommendation via Disentangled Graph Contrastive Learning

35 citations · 147 across the 15 of their papers we have counts for

collaborators

16 papers

cs.CV2024

Temporal Residual Guided Diffusion Framework for Event-Driven Video Reconstruction

Lin Zhu, Yunlong Zheng, Yijun Zhang +3

Event-based video reconstruction has garnered increasing attention due to its advantages, such as high dynamic range and rapid motion capture capabilities. However, current methods…

cs.RO202435 cited

Social Force Embedded Mixed Graph Convolutional Network for Multi-class Trajectory Prediction

Quancheng Du, Xiao Wang, Shouguo Yin +2

Accurate prediction of agent motion trajectories is crucial for autonomous driving, contributing to the reduction of collision risks in human-vehicle interactions and ensuring ampl…

cs.RO202418 cited

S4TP: Social-Suitable and Safety-Sensitive Trajectory Planning for Autonomous Vehicles

Xiao Wang, Ke Tang, Xingyuan Dai +5

In public roads, autonomous vehicles (AVs) face the challenge of frequent interactions with human-driven vehicles (HDVs), which render uncertain driving behavior due to varying soc…

cs.AI2024

Multi-Scale Subgraph Contrastive Learning

Yanbei Liu, Yu Zhao, Xiao Wang +2

Graph-level contrastive learning, aiming to learn the representations for each graph by contrasting two augmented graphs, has attracted considerable attention. Previous studies usu…

cs.CY202435 cited

The Survey on Multi-Source Data Fusion in Cyber-Physical-Social Systems:Foundational Infrastructure for Industrial Metaverses and Industries 5.0

Xiao Wang, Yutong Wang, Jing Yang +4

As the concept of Industries 5.0 develops, industrial metaverses are expected to operate in parallel with the actual industrial processes to offer ``Human-Centric" Safe, Secure, Su…

cs.LG20243 cited

CLIP the Bias: How Useful is Balancing Data in Multimodal Learning?

Ibrahim Alabdulmohsin, Xiao Wang, Andreas Steiner +3

We study the effectiveness of data-balancing for mitigating biases in contrastive language-image pretraining (CLIP), identifying areas of strength and limitation. First, we reaffir…