Z
Zequn Qin
Researcher at Northwestern Polytechnical University
Publications - 22
Citations - 501
Zequn Qin is an academic researcher from Northwestern Polytechnical University. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 5, co-authored 16 publications receiving 177 citations. Previous affiliations of Zequn Qin include Zhejiang University.
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Ultra Fast Structure-aware Deep Lane Detection
Zequn Qin,Huanyu Wang,Xi Li +2 more
TL;DR: A novel, simple, yet effective formulation aiming at extremely fast speed and challenging scenarios, which treats the process of lane detection as a row-based selecting problem using global features and proposes a structural loss to explicitly model the structure of lanes.
Book ChapterDOI
Ultra Fast Structure-aware Deep Lane Detection
Zequn Qin,Huanyu Wang,Xi Li +2 more
TL;DR: In this paper, Liu et al. proposed a novel, simple, yet effective formulation aiming at extremely fast speed and challenging scenarios, which treated the process of lane detection as a row-based selecting problem using global features.
Journal ArticleDOI
Spectral Embedded Adaptive Neighbors Clustering
TL;DR: A novel linear space embedded clustering method is proposed, which uses adaptive neighbors to address the above-mentioned problems and linearity regularization is used to make the data representation a linear embedded spectral.
Journal ArticleDOI
Multitask Attention Network for Lane Detection and Fitting
TL;DR: This work proposes a novel multitask method that integrates the ability to model semantic information of CNN and the strong localization ability provided by handcrafted features and predicts the position of vanishing line.
Proceedings ArticleDOI
Convolutional 2D LDA for Nonlinear Dimensionality Reduction
TL;DR: A novel convolutional two-dimensional linear discriminant analysis (2D LDA) method for data representation and a specially designed Convolutional Neural Networks (CNN) is presented, which can be proved having the equivalent objective function with common 2D L DA.