1 code implementation • 22 Nov 2023 • ZiHao Zhou, Bin Hu, Chenyang Zhao, Pu Zhang, Bin Liu
By incorporating the guidance from the teacher agent, the student agent can distill the prior knowledge of the LLM into its own model.
1 code implementation • 19 Aug 2023 • Song Tang, Chuang Li, Pu Zhang, RongNian Tang
In this paper, we propose a new recurrent cell, SwinLSTM, which integrates Swin Transformer blocks and the simplified LSTM, an extension that replaces the convolutional structure in ConvLSTM with the self-attention mechanism.
no code implementations • 25 Jun 2023 • Yuning Wang, Pu Zhang, Lei Bai, Jianru Xue
Scene information plays a crucial role in trajectory forecasting systems for autonomous driving by providing semantic clues and constraints on potential future paths of traffic agents.
1 code implementation • 6 Jun 2023 • Bin Hu, Chenyang Zhao, Pu Zhang, ZiHao Zhou, Yuanhang Yang, Zenglin Xu, Bin Liu
In this paper, we explore how to enable intelligent cost-effective interactions between the agent and an LLM.
no code implementations • CVPR 2023 • Yuning Wang, Pu Zhang, Lei Bai, Jianru Xue
In this paper, we focus on dealing with the long-tail phenomenon in trajectory prediction.
1 code implementation • 16 Mar 2023 • Pu Zhang, Tianhua Chen, Bin Liu
To achieve accurate fine-grained detection, one needs to employ a large enough model and a vast amount of data annotations.
1 code implementation • ICCV 2023 • Song Tang, Chuang Li, Pu Zhang, RongNian Tang
In this paper, we propose a new recurrent cell, SwinLSTM, which integrates Swin Transformer blocks and the simplified LSTM, an extension that replaces the convolutional structure in ConvLSTM with the self-attention mechanism.
Ranked #7 on Video Prediction on Moving MNIST
1 code implementation • 2 Nov 2022 • Jianwu Fang, Chen Zhu, Pu Zhang, Hongkai Yu, Jianru Xue
Heterogeneous trajectory forecasting is critical for intelligent transportation systems, but it is challenging because of the difficulty of modeling the complex interaction relations among the heterogeneous road agents as well as their agent-environment constraints.
no code implementations • 3 Feb 2022 • Pu Zhang, Lei Bai, Jianru Xue, Jianwu Fang, Nanning Zheng, Wanli Ouyang
Trajectories obtained from object detection and tracking are inevitably noisy, which could cause serious forecasting errors to predictors built on ground truth trajectories.
1 code implementation • 15 Mar 2019 • Jianru Xue, Jianwu Fang, Tao Li, Bohua Zhang, Pu Zhang, Zhen Ye, Jian Dou
Instead, BLVD aims to provide a platform for the tasks of dynamic 4D (3D+temporal) tracking, 5D (4D+interactive) interactive event recognition and intention prediction.
1 code implementation • CVPR 2019 • Pu Zhang, Wanli Ouyang, Pengfei Zhang, Jianru Xue, Nanning Zheng
In order to address this issue, we propose a data-driven state refinement module for LSTM network (SR-LSTM), which activates the utilization of the current intention of neighbors, and jointly and iteratively refines the current states of all participants in the crowd through a message passing mechanism.