Search Results for author: Lei Qiao

Found 9 papers, 3 papers with code

Unsupervised Learning of Accurate Siamese Tracking

1 code implementation CVPR 2022 Qiuhong Shen, Lei Qiao, Jinyang Guo, Peixia Li, Xin Li, Bo Li, Weitao Feng, Weihao Gan, Wei Wu, Wanli Ouyang

As unlimited self-supervision signals can be obtained by tracking a video along a cycle in time, we investigate evolving a Siamese tracker by tracking videos forward-backward.

Visual Object Tracking

Backbone is All Your Need: A Simplified Architecture for Visual Object Tracking

1 code implementation10 Mar 2022 BoYu Chen, Peixia Li, Lei Bai, Lei Qiao, Qiuhong Shen, Bo Li, Weihao Gan, Wei Wu, Wanli Ouyang

Exploiting a general-purpose neural architecture to replace hand-wired designs or inductive biases has recently drawn extensive interest.

Visual Object Tracking

STUaNet: Understanding uncertainty in spatiotemporal collective human mobility

no code implementations9 Feb 2021 Zhengyang Zhou, Yang Wang, Xike Xie, Lei Qiao, Yuantao Li

The high dynamics and heterogeneous interactions in the complicated urban systems have raised the issue of uncertainty quantification in spatiotemporal human mobility, to support critical decision-makings in risk-aware web applications such as urban event prediction where fluctuations are of significant interests.

Uncertainty Quantification

Learning Collision-Free Space Detection from Stereo Images: Homography Matrix Brings Better Data Augmentation

no code implementations14 Dec 2020 Rui Fan, Hengli Wang, Peide Cai, Jin Wu, Mohammud Junaid Bocus, Lei Qiao, Ming Liu

Therefore, this paper mainly explores an effective training data augmentation approach that can be employed to improve the overall DCNN performance, when additional images captured from different views are available.

Data Augmentation Semantic Segmentation

Robust perfect equilibrium in large games

no code implementations30 Dec 2019 Enxian Chen, Lei Qiao, Xiang Sun, Yeneng Sun

This paper proposes a new equilibrium concept "robust perfect equilibrium" for non-cooperative games with a continuum of players, incorporating three types of perturbations.

Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applications

1 code implementation2 Jul 2019 Moming Duan, Duo Liu, Xianzhang Chen, Yujuan Tan, Jinting Ren, Lei Qiao, Liang Liang

However, unlike the common training dataset, the data distribution of the edge computing system is imbalanced which will introduce biases in the model training and cause a decrease in accuracy of federated learning applications.

Data Augmentation Edge-computing +2

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