activity
20152021
most citedDeep Learning based Monocular Depth Prediction: Datasets, Methods and Applications

8 citations · 11 across the 3 of their papers we have counts for

collaborators

6 papers

eess.IV20211 cited

Multi-Attention Generative Adversarial Network for Remote Sensing Image Super-Resolution

Meng Xu, Zhihao Wang, Jiasong Zhu +2

Image super-resolution (SR) methods can generate remote sensing images with high spatial resolution without increasing the cost, thereby providing a feasible way to acquire high-re…

cs.CV20208 cited

Deep Learning based Monocular Depth Prediction: Datasets, Methods and Applications

Qing Li, Jiasong Zhu, Jun Liu +4

Estimating depth from RGB images can facilitate many computer vision tasks, such as indoor localization, height estimation, and simultaneous localization and mapping (SLAM). Recent…

cs.CV2019

Enhancing Remote Sensing Image Retrieval with Triplet Deep Metric Learning Network

Rui Cao, Qian Zhang, Jiasong Zhu +4

With the rapid growing of remotely sensed imagery data, there is a high demand for effective and efficient image retrieval tools to manage and exploit such data. In this letter, we…

cs.CV2019

Relative Geometry-Aware Siamese Neural Network for 6DOF Camera Relocalization

Qing Li, Jiasong Zhu, Rui Cao +5

6DOF camera relocalization is an important component of autonomous driving and navigation. Deep learning has recently emerged as a promising technique to tackle this problem. In th…

cs.LG2018

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.CV20152 cited

LOAD: Local Orientation Adaptive Descriptor for Texture and Material Classification

Xianbiao Qi, Guoying Zhao, Linlin Shen +2

In this paper, we propose a novel local feature, called Local Orientation Adaptive Descriptor (LOAD), to capture regional texture in an image. In LOAD, we proposed to define point…