activity
20152019
most citedNatural Scene Recognition Based on Superpixels and Deep Boltzmann Machines

6 citations · 11 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2019

Toward Learning a Unified Many-to-Many Mapping for Diverse Image Translation

Wenju Xu, Shawn Keshmiri, Guanghui Wang

Image-to-image translation, which translates input images to a different domain with a learned one-to-one mapping, has achieved impressive success in recent years. The success of t…

stat.ML20171 cited

BPGrad: Towards Global Optimality in Deep Learning via Branch and Pruning

Ziming Zhang, Yuanwei Wu, Guanghui Wang

Understanding the global optimality in deep learning (DL) has been attracting more and more attention recently. Conventional DL solvers, however, have not been developed intentiona…

cs.CV20172 cited

Vision-based Real-Time Aerial Object Localization and Tracking for UAV Sensing System

Yuanwei Wu, Yao Sui, Guanghui Wang

The paper focuses on the problem of vision-based obstacle detection and tracking for unmanned aerial vehicle navigation. A real-time object localization and tracking strategy from…

cs.CV20156 cited

Natural Scene Recognition Based on Superpixels and Deep Boltzmann Machines

Jinfu Yang, Jingyu Gao, Guanghui Wang +1

The Deep Boltzmann Machines (DBM) is a state-of-the-art unsupervised learning model, which has been successfully applied to handwritten digit recognition and, as well as object rec…

cs.CV20152 cited

A Novel Feature Extraction Method for Scene Recognition Based on Centered Convolutional Restricted Boltzmann Machines

Jingyu Gao, Jinfu Yang, Guanghui Wang +1

Scene recognition is an important research topic in computer vision, while feature extraction is a key step of object recognition. Although classical Restricted Boltzmann machines…