activity
20222026
most citedExploring Radar Data Representations in Autonomous Driving: A Comprehensive Review

66 citations · 73 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2026

Traffic-CBM: A Structurally Interpretable Multimodal Framework for Encrypted Traffic Classification

Honglei Jin, Wenshuo Chen, Shaofeng Liang +6

Encrypted traffic classification has achieved strong performance, but its decision process remains difficult to interpret. Existing methods usually combine flow statistics, packet…

cs.CV2025★ 1 cited

ANT: Adaptive Neural Temporal-Aware Text-to-Motion Model

Wenshuo Chen, Kuimou Yu, Haozhe Jia +8

While diffusion models advance text-to-motion generation, their static semantic conditioning ignores temporal-frequency demands: early denoising requires structural semantics for m…

cs.CV2025

Physics-Informed Representation Alignment for Sparse Radio-Map Reconstruction

Haozhe Jia, Wenshuo Chen, Zhihui Huang +7

Radio map reconstruction is essential for enabling advanced applications, yet challenges such as complex signal propagation and sparse observational data hinder accurate reconstruc…

cs.CV2023★ 66 cited

Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Review

Shanliang Yao, Runwei Guan, Zitian Peng +10

With the rapid advancements of sensor technology and deep learning, autonomous driving systems are providing safe and efficient access to intelligent vehicles as well as intelligen…

cs.CV2023★ 5 cited

WaterScenes: A Multi-Task 4D Radar-Camera Fusion Dataset and Benchmarks for Autonomous Driving on Water Surfaces

Shanliang Yao, Runwei Guan, Zhaodong Wu +11

Autonomous driving on water surfaces plays an essential role in executing hazardous and time-consuming missions, such as maritime surveillance, survivors rescue, environmental moni…

cs.IT2022★ 1 cited

Quantify the Causes of Causal Emergence: Critical Conditions of Uncertainty and Asymmetry in Causal Structure

Liye Jia, Fengyufan Yang, Ka Lok Man +4

Beneficial to advanced computing devices, models with massive parameters are increasingly employed to extract more information to enhance the precision in describing and predicting…