no code implementations • 1 Mar 2024 • Jianwu Fang, Lei-Lei Li, Junfei Zhou, Junbin Xiao, Hongkai Yu, Chen Lv, Jianru Xue, Tat-Seng Chua
This model involves a contrastive interaction loss to learn the pair co-occurrence of normal, near-accident, accident frames with the corresponding text descriptions, such as accident reasons, prevention advice, and accident categories.
1 code implementation • 13 Feb 2024 • Peining Shen, Jianwu Fang, Hongkai Yu, Jianru Xue
In this work, we explore the interpretability of behavior prediction of target vehicles by an Episodic Memory implanted Neural Decision Tree (abbrev.
no code implementations • 30 Aug 2023 • Jianwu Fang, iahuan Qiao, Jianru Xue, Zhengguo Li
We present the first survey on Vision-TAD in the deep learning era and the first-ever survey for Vision-TAA.
1 code implementation • 1 Aug 2023 • Tianci Zhao, Xue Bai, Jianwu Fang, Jianru Xue
In this work, we explore the network connection gating mechanism for driver attention prediction (Gate-DAP).
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.
no code implementations • 10 Apr 2023 • Kang Zhao, Jianru Xue, Xiangning Meng, Gengxin Li, Mengsen Wu
One major issue in learning-based model predictive control (MPC) for autonomous driving is the contradiction between the system model's prediction accuracy and computation efficiency.
1 code implementation • 7 Apr 2023 • Xuyang Li, Jianwu Fang, Kai Du, Kuizhi Mei, Jianru Xue
This paper focuses on the continuous control of the unmanned aerial vehicle (UAV) based on a deep reinforcement learning method for a large-scale 3D complex environment.
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 • 19 Dec 2022 • Jianwu Fang, Lei-Lei Li, Kuan Yang, Zhedong Zheng, Jianru Xue, Tat-Seng Chua
In particular, the text description provides a dense semantic description guidance for the primary context of the traffic scene, while the driver attention provides a traction to focus on the critical region closely correlating with safe driving.
no code implementations • 2 Nov 2022 • Jie Bai, Xin Fang, Jianwu Fang, Jianru Xue, Changwei Yuan
To this end, we formulate a deep virtual to real distillation framework by introducing the synthetic data that can be generated conveniently, and borrow the abundant information of pedestrian movement in synthetic videos for the pedestrian crossing prediction in real data with a simple and lightweight implementation.
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 • 1 Nov 2022 • Jianwu Fang, Fan Wang, Jianru Xue, Tat-Seng Chua
Behavioral Intention Prediction (BIP) simulates such a human consideration process and fulfills the early prediction of specific behaviors.
no code implementations • 10 Oct 2022 • Xingyu Chen, Jianru Xue, Jianwu Fang, Yuxin Pan, Nanning Zheng
In this paper, we propose a lightweight system, RDS-SLAM, based on ORB-SLAM2, which can accurately estimate poses and build semantic maps at object level for dynamic scenarios in real time using only one commonly used Intel Core i7 CPU.
no code implementations • 10 Oct 2022 • Xingyu Chen, Jianru Xue, Shanmin Pang
The proposed sparse semantic map-based localization approach is robust against occlusion and long-term appearance changes in the environments.
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.
no code implementations • 7 Nov 2021 • Pengfei Zhang, Cuiling Lan, Wenjun Zeng, Junliang Xing, Jianru Xue, Nanning Zheng
Skeleton data is of low dimension.
no code implementations • 25 Mar 2020 • Rixing Zhu, Jianwu Fang, Hongke Xu, Hongkai Yu, Jianru Xue
Vehicle re-identification (Re-ID) has become a popular research topic owing to its practicability in intelligent transportation systems.
1 code implementation • 18 Dec 2019 • Jianwu Fang, Dingxin Yan, Jiahuan Qiao, Jianru Xue, Hongkai Yu
1) With the semantic images, we introduce their semantic context features and verified the manifest promotion effect for helping the driver attention prediction, where the semantic context features are modeled by a graph convolution network (GCN) on semantic images; 2) We fuse the semantic context features of semantic images and the features of RGB frames in an attentive strategy, and the fused details are transferred over frames by a convolutional LSTM module to obtain the attention map of each video frame with the consideration of historical scene variation in driving situations; 3) The superiority of the proposed method is evaluated on our previously collected dataset (named as DADA-2000) and two other challenging datasets with state-of-the-art methods.
no code implementations • 3 Sep 2019 • Pengfei Zhang, Jianru Xue, Cuiling Lan, Wen-Jun Zeng, Zhanning Gao, Nanning Zheng
For an RNN block, an EleAttG is used for adaptively modulating the input by assigning different levels of importance, i. e., attention, to each element/dimension of the input.
Ranked #3 on Skeleton Based Action Recognition on SYSU 3D
no code implementations • 23 Apr 2019 • Jianwu Fang, Dingxin Yan, Jiahuan Qiao, Jianru Xue, He Wang, Sen Li
Driver attention prediction is currently becoming the focus in safe driving research community, such as the DR(eye)VE project and newly emerged Berkeley DeepDrive Attention (BDD-A) database in critical situations.
2 code implementations • CVPR 2020 • Pengfei Zhang, Cuiling Lan, Wen-Jun Zeng, Junliang Xing, Jianru Xue, Nanning Zheng
Skeleton-based human action recognition has attracted great interest thanks to the easy accessibility of the human skeleton data.
Ranked #1 on Skeleton Based Action Recognition on SYSU 3D
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.
no code implementations • ECCV 2018 • Pengfei Zhang, Jianru Xue, Cuiling Lan, Wen-Jun Zeng, Zhanning Gao, Nanning Zheng
We propose adding a simple yet effective Element-wiseAttention Gate (EleAttG) to an RNN block (e. g., all RNN neurons in a network layer) that empowers the RNN neurons to have the attentiveness capability.
Ranked #98 on Skeleton Based Action Recognition on NTU RGB+D
1 code implementation • 22 May 2018 • Shanmin Pang, Jin Ma, Jianru Xue, Jihua Zhu, Vicente Ordonez
We show that by considering each deep feature as a heat source, our unsupervised aggregation method is able to avoid over-representation of \emph{bursty} features.
2 code implementations • 20 Apr 2018 • Pengfei Zhang, Cuiling Lan, Junliang Xing, Wen-Jun Zeng, Jianru Xue, Nanning Zheng
In order to alleviate the effects of view variations, this paper introduces a novel view adaptation scheme, which automatically determines the virtual observation viewpoints in a learning based data driven manner.
Ranked #1 on Skeleton Based Action Recognition on UWA3D
no code implementations • CVPR 2017 • Zhanning Gao, Gang Hua, Dong-Qing Zhang, Nebojsa Jojic, Le Wang, Jianru Xue, Nanning Zheng
We develop a unified framework for complex event retrieval, recognition and recounting.
1 code implementation • ICCV 2017 • Pengfei Zhang, Cuiling Lan, Junliang Xing, Wen-Jun Zeng, Jianru Xue, Nanning Zheng
Rather than re-positioning the skeletons based on a human defined prior criterion, we design a view adaptive recurrent neural network (RNN) with LSTM architecture, which enables the network itself to adapt to the most suitable observation viewpoints from end to end.
Ranked #6 on Skeleton Based Action Recognition on SYSU 3D
no code implementations • 5 Apr 2016 • Zhanning Gao, Gang Hua, Dongqing Zhang, Jianru Xue, Nanning Zheng
Event retrieval and recognition in a large corpus of videos necessitates a holistic fixed-size visual representation at the video clip level that is comprehensive, compact, and yet discriminative.
no code implementations • ICCV 2015 • Yuanliu liu, Zejian yuan, Badong Chen, Jianru Xue, Nanning Zheng
In this paper we address the problem of inferring the color composition of the intrinsic reflectance of objects, where the shadows and highlights may change the observed color dramatically.