1 code implementation • 26 Nov 2021 • Qifan Xue, Shengyi Li, Xuanpeng Li, Jingwen Zhao, Weigong Zhang
HMNet first infers the hierarchical difference on motions to encode physically compliant patterns with high expressivity of moving trends and driving intentions.
1 code implementation • 27 Jan 2022 • Feng Yang, Yichao Cao, Qifan Xue, Shuai Jin, Xuanpeng Li, Weigong Zhang
Learning a powerful representation from point clouds is a fundamental and challenging problem in the field of computer vision.
1 code implementation • 18 Mar 2024 • Yezhuo Zhang, Zinan Zhou, Xuanpeng Li
In the domain of Specific Emitter Identification (SEI), it is recognized that transmitters can be distinguished through the impairments of their radio frequency front-end, commonly referred to as Radio Frequency Fingerprint (RFF) features.
no code implementations • 13 Nov 2016 • Xuanpeng Li, Rachid Belaroussi
The bundle of geometry and appearance in computer vision has proven to be a promising solution for robots across a wide variety of applications.
no code implementations • 27 Jan 2021 • Jingwen Zhao, Xuanpeng Li, Qifan Xue, Weigong Zhang
In this paper, we present a Spatial-Channel Transformer Network for trajectory prediction with attention functions.
no code implementations • 22 Feb 2021 • Qifan Xue, Xuanpeng Li, Weigong Zhang
Trajectory prediction plays a pivotal role in the field of intelligent vehicles.
no code implementations • 11 Jul 2023 • Shengyi Li, Qifan Xue, Yezhuo Zhang, Xuanpeng Li
To leverage causal features for prediction, we propose a Causal Inspired Learning Framework (CILF), which includes three steps: 1) extracting domain-invariant causal feature by means of an invariance loss, 2) extracting domain variant feature by domain contrastive learning, and 3) separating domain-variant causal and non-causal feature by encouraging causal sufficiency.