中文版 | English

在结果中检索

CASIA OpenIR

浏览/检索结果: 共5条，第1-5条 帮助

限定条件        
已选(0)清除 条数/页：   排序方式：
Domain adaptive object detection with model-agnostic knowledge transferring 期刊论文
Neural Networks, 2023, 页码: 213-227
作者:  Tian Kun;  Zhang Chenghao;  Wang Ying;  Xiang Shiming
Adobe PDF(3116Kb)  |  收藏  |  浏览/下载：17/9  |  提交时间：2024/05/28
Triplet Adversarial Domain Adaptation for Pixel-Level Classification of VHR Remote Sensing Images 期刊论文
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2020, 卷号: 58, 期号: 5, 页码: 3558-3573
作者:  Yan, Liang;  Fan, Bin;  Liu, Hongmin;  Huo, Chunlei;  Xiang, Shiming;  Pan, Chunhong
Adobe PDF(6348Kb)  |  收藏  |  浏览/下载：414/101  |  提交时间：2020/06/22
Domain adaptation (DA)  pixel-level classification  self-training  triplet adversarial learning  very high resolution (VHR)  
Automatic Building Rooftop Extraction From Aerial Images via Hierarchical RGB-D Priors 期刊论文
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2018, 卷号: 56, 期号: 12, 页码: 7369-7387
作者:  Xu, Shibiao;  Pan, Xingjia;  Li, Er;  Wu, Baoyuan;  Bu, Shuhui;  Dong, Weiming;  Xiang, Shiming;  Zhang, Xiaopeng
浏览  |  Adobe PDF(30927Kb)  |  收藏  |  浏览/下载：461/40  |  提交时间：2019/07/12
High-order conditional random field (CRF)  multilevel segmentation  RGB-D priors  rooftop extraction  
Blind image quality assessment via learnable attention-based pooling 期刊论文
PATTERN RECOGNITION, 2019, 卷号: 91, 页码: 332-344
作者:  Gu, Jie;  Meng, Gaofeng;  Xiang, Shiming;  Pan, Chunhong
浏览  |  Adobe PDF(3081Kb)  |  收藏  |  浏览/下载：524/195  |  提交时间：2019/05/15
Image quality assessment  Perceptual image quality  Visual attention  Convolutional neural network  Learnable pooling  
Semantic labeling in very high resolution images via a self-cascaded convolutional neural network 期刊论文
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2018, 卷号: 145, 期号: 1, 页码: 78-95
作者:  Liu, Yongcheng;  Fan, Bin;  Wang, Lingfeng;  Bai, Jun;  Xiang, Shiming;  Pan, Chunhong
浏览  |  Adobe PDF(1679Kb)  |  收藏  |  浏览/下载：531/111  |  提交时间：2019/01/08
Semantic labeling  Convolutional neural networks (CNNs)  Multi-scale contexts  End-to-end