activity
20162019
most citedRobust Lane Detection from Continuous Driving Scenes Using Deep Neural Networks

358 citations · 399 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2019★ 358 cited

Robust Lane Detection from Continuous Driving Scenes Using Deep Neural Networks

Qin Zou, Hanwen Jiang, Qiyu Dai +3

Lane detection in driving scenes is an important module for autonomous vehicles and advanced driver assistance systems. In recent years, many sophisticated lane detection methods h…

cs.LG2018★ 35 cited

Deep Learning-Based Gait Recognition Using Smartphones in the Wild

Qin Zou, Yanling Wang, Qian Wang +2

Compared to other biometrics, gait is difficult to conceal and has the advantage of being unobtrusive. Inertial sensors, such as accelerometers and gyroscopes, are often used to ca…

cs.CV2018★ 1 cited

Dating Ancient Paintings of Mogao Grottoes Using Deeply Learnt Visual Codes

Qingquan Li, Qin Zou, De Ma +2

Cultural heritage is the asset of all the peoples of the world. The preservation and inheritance of cultural heritage is conducive to the progress of human civilization. In northwe…

cs.CV2018

Improved Deep Hashing with Soft Pairwise Similarity for Multi-label Image Retrieval

Zheng Zhang, Qin Zou, Yuewei Lin +2

Hash coding has been widely used in the approximate nearest neighbor search for large-scale image retrieval. Recently, many deep hashing methods have been proposed and shown largel…

cs.CV2016★ 1 cited

Robust Gait Recognition by Integrating Inertial and RGBD Sensors

Qin Zou, Lihao Ni, Qian Wang +2

Gait has been considered as a promising and unique biometric for person identification. Traditionally, gait data are collected using either color sensors, such as a CCD camera, dep…

cs.CV2016★ 4 cited

Who Leads the Clothing Fashion: Style, Color, or Texture? A Computational Study

Qin Zou, Zheng Zhang, Qian Wang +3

It is well known that clothing fashion is a distinctive and often habitual trend in the style in which a person dresses. Clothing fashions are usually expressed with visual stimuli…