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- Xi'an Institute of Optics and Precision MechanicsCN46 papers
- Chinese Academy of SciencesCN40 papers
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- University of Chinese Academy of SciencesCN10 papers
- ITMO UniversityRU8 papers
- Swinburne University of TechnologyAU8 papers
- RMIT UniversityAU7 papers
- University of Electronic Science and Technology of ChinaCN7 papers
- Institut National de la Recherche ScientifiqueCA6 papers
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11 papers · 1 filter
Object Discovery via Cohesion Measurement
Guanjun Guo, Hanzi Wang, Wan-Lei Zhao +2
Color and intensity are two important components in an image. Usually, groups of image pixels, which are similar in color or intensity, are an informative representation for an obj…
Remote Sensing Image Scene Classification: Benchmark and State of the Art
Gong Cheng, Junwei Han, Xiaoqiang Lu
Remote sensing image scene classification plays an important role in a wide range of applications and hence has been receiving remarkable attention. During the past years, signific…
AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification
Gui-Song Xia, Jingwen Hu, Fan Hu +4
Aerial scene classification, which aims to automatically label an aerial image with a specific semantic category, is a fundamental problem for understanding high-resolution remote…
Convolution in Convolution for Network in Network
Yanwei Pang, Manli Sun, Xiaoheng Jiang +1
Network in Netwrok (NiN) is an effective instance and an important extension of Convolutional Neural Network (CNN) consisting of alternating convolutional layers and pooling layers…
Cascaded Subpatch Networks for Effective CNNs
Xiaoheng Jiang, Yanwei Pang, Manli Sun +1
Conventional Convolutional Neural Networks (CNNs) use either a linear or non-linear filter to extract features from an image patch (region) of spatial size (Typically…
Learning Multilayer Channel Features for Pedestrian Detection
Jiale Cao, Yanwei Pang, Xuelong Li
Pedestrian detection based on the combination of Convolutional Neural Network (i.e., CNN) and traditional handcrafted features (i.e., HOG+LUV) has achieved great success. Generally…