no code implementations • IEEE Transactions on Cybernetics 2023 • Lisha Cui, Pei Lv, Xiaoheng Jiang, Zhimin Gao, Bing Zhou, Luming Zhang, Ling Shao, Mingliang Xu
State-of-the-art object detectors usually progressively downsample the input image until it is represented by small feature maps, which loses the spatial information and compromises the representation of small objects.
Ranked #1 on Traffic Sign Detection on TT100K
no code implementations • 6 Oct 2022 • Zhimin Gao, Peitao Wang, Pei Lv, Xiaoheng Jiang, Qidong Liu, Pichao Wang, Mingliang Xu, Wanqing Li
Besides, these methods directly calculate the pair-wise global self-attention equally for all the joints in both the spatial and temporal dimensions, undervaluing the effect of discriminative local joints and the short-range temporal dynamics.
1 code implementation • 21 Jul 2022 • Yuzhen Zhang, Wentong Wang, Weizhi Guo, Pei Lv, Mingliang Xu, Wei Chen, Dinesh Manocha
We present a trajectory prediction approach with respect to traffic lights, D2-TPred, which uses a spatial dynamic interaction graph (SDG) and a behavior dependency graph (BDG) to handle the problem of discontinuous dependency in the spatial-temporal space.
no code implementations • 8 Mar 2022 • Ruijie Zhu, Lulu Li, Shuning Wu, Pei Lv, Yafai Li, Mingliang Xu
Existing approaches of MAS are largely based on Multi-Agent Deep Reinforcement Learning (MADRL).
no code implementations • 20 Dec 2021 • Jinfeng Wei, Yunxin Wang, Mengli Guo, Pei Lv, Xiaoshan Yang, Mingliang Xu
Graph convolutional networks (GCNs) based methods have achieved advanced performance on skeleton-based action recognition task.
no code implementations • 5 Dec 2021 • Pei Lv, Wentong Wang, Yunxin Wang, Yuzhen Zhang, Mingliang Xu, Changsheng Xu
In detail, when modeling social interaction, we propose a new \emph{social soft attention function}, which fully considers various interaction factors among pedestrians.
no code implementations • 14 Oct 2021 • Pei Lv, Suqi Hu, Tianran Hao
Inspired by the habit of observing things by the human, we propose a new method by comparing the initial proposals and the extension ones to optimize those initial proposals.
no code implementations • 14 Jun 2021 • Pei Lv, Jianqi Fan, Xixi Nie, WeiMing Dong, Xiaoheng Jiang, Bing Zhou, Mingliang Xu, Changsheng Xu
This framework leverages user interactions to retouch and rank images for aesthetic assessment based on deep reinforcement learning (DRL), and generates personalized aesthetic distribution that is more in line with the aesthetic preferences of different users.
no code implementations • 29 Apr 2021 • Pei Lv, Qingqing Yu, Boya Xu, Chaochao Li, Bing Zhou, Mingliang Xu
In this paper, we propose an Emotional contagion-aware Deep reinforcement learning model for Antagonistic Crowd Simulation (ACSED).
no code implementations • 22 Feb 2021 • Pei Lv, Quan Zhang, Boya Xu, Ran Feng, Chaochao Li, Junxiao Xue, Bing Zhou, Mingliang Xu
Corona Virus Disease 2019 (COVID-19), due to its extremely high infectivity, has been spreading rapidly around the world and bringing huge influence to socioeconomic development as well as people's daily life.
Social and Information Networks Physics and Society Populations and Evolution
no code implementations • 26 Jan 2021 • Pei Lv, Hui Wei, Tianxin Gu, Yuzhen Zhang, Xiaoheng Jiang, Bing Zhou, Mingliang Xu
Based on this unique description, we develop one novel trajectory prediction method, called social probability.
no code implementations • 1 Nov 2019 • Chaochao Li, Pei Lv, Mingliang Xu, Xinyu Wang, Dinesh Manocha, Bing Zhou, Meng Wang
We update this map dynamically based on the agents in the environment and prior trajectory of a pedestrian.
no code implementations • 24 Sep 2019 • Pei Lv, Haiyu Yu, Junxiao Xue, Junjin Cheng, Lisha Cui, Bing Zhou, Mingliang Xu, Yi Yang
On ILSVRC 2016, the proposed method yields the Top-1 localization error of 48. 65\%, which outperforms previous results by 2. 75\%.
no code implementations • 21 Aug 2018 • Pei Lv, Shunhua Liu, Mingliang Xu, Bing Zhou
This paper proposes a method based on repulsive forces and sparse reconstruction for the detection and location of abnormal events in crowded scenes.
1 code implementation • 18 May 2018 • Lisha Cui, Rui Ma, Pei Lv, Xiaoheng Jiang, Zhimin Gao, Bing Zhou, Mingliang Xu
The performance of small object detection, however, is still less than satisfactory because of the deficiency of semantic information on shallow feature maps.
no code implementations • 3 May 2018 • Pei Lv, Meng Wang, Yongbo Xu, Ze Peng, Junyi Sun, Shimei Su, Bing Zhou, Mingliang Xu
When assessing whether an image is of high or low quality, it is indispensable to take personal preference into account.
no code implementations • 2 May 2018 • Jing Wang, Ze Peng, Pei Lv, Junyi Sun, Bing Zhou, Mingliang Xu
The first branch predicts the confidence maps of joints and uses a geometrical transform kernel to propagate information between neighboring joints at the confidence level.
no code implementations • 3 Mar 2018 • Mingliang Xu, Zhaoyang Ge, Xiaoheng Jiang, Gaoge Cui, Pei Lv, Bing Zhou, Changsheng Xu
DigCrowd first uses the depth information of an image to segment the scene into a far-view region and a near-view region.