no code implementations • 18 Mar 2024 • Hanxi Wan, Pei Li, Arpan Kusari
With the advent of universal function approximators in the domain of reinforcement learning, the number of practical applications leveraging deep reinforcement learning (DRL) has exploded.
no code implementations • 5 Feb 2024 • Wei Song, Pei Li, Man Wang
To address the challenges of low detection accuracy and high false positive rates of transmission lines in UAV (Unmanned Aerial Vehicle) images, we explore the linear features and spatial distribution.
no code implementations • 10 Oct 2022 • Pei Li, Zhijun Liu, Luyi Chang, Jialiang Peng, Yi Wu
This is because most drones still use centralized cloud-based data processing, which may lead to leakage of data collected by drones.
no code implementations • 28 Jul 2022 • Pei Li, Huizhong Guo, Shan Bao, Arpan Kusari
To evaluate pedestrian safety proactively, surrogate safety measures (SSMs) have been widely used in traffic conflict-based studies as they do not require historical crashes as inputs.
no code implementations • 15 Feb 2022 • Pei Li, Lingyi Wang, Wei Wu, Fuhui Zhou, Baoyun Wang, Qihui Wu
In this paper, we propose a novel graph neural networks (GNN) based approach that can map the considered system into a specific graph structure and achieve the optimal solution in a low complexity manner.
1 code implementation • 24 Oct 2021 • Pei Li, Nir Shlezinger, Haiyang Zhang, Baoyun Wang, Yonina C. Eldar
The common framework for graph signal compression is based on sampling, resulting in a set of continuous-amplitude samples, which in turn have to be quantized into a finite bit representation.
no code implementations • 7 Apr 2019 • Samuel Grieggs, Bingyu Shen, Greta Rauch, Pei Li, Jiaqi Ma, David Chiang, Brian Price, Walter J. Scheirer
The subtleties of human perception, as measured by vision scientists through the use of psychophysics, are important clues to the internal workings of visual recognition.
no code implementations • 29 May 2018 • Pei Li, Loreto Prieto, Domingo Mery, Patrick Flynn
Although face recognition systems have achieved impressive performance in recent years, the low-resolution face recognition (LRFR) task remains challenging, especially when the LR faces are captured under non-ideal conditions, as is common in surveillance-based applications.
no code implementations • 29 May 2018 • Pei Li, Loreto Prieto, Domingo Mery, Patrick Flynn
Its applications lie widely in the real-world environment when high-resolution or high-quality images are hard to capture.
no code implementations • 30 Apr 2018 • Pei Li, Bingyu Shen, Weishan Dong
Both local and global deep features are extracted using VGG model\cite{Simonyan14c}, which are fused later for more robust system performance.
no code implementations • SEMEVAL 2017 • Sheng Zhang, Jiajun Cheng, Hui Wang, Xin Zhang, Pei Li, Zhaoyun Ding
We describes deep neural networks frameworks in this paper to address the community question answering (cQA) ranking task (SemEval-2017 task 3).