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Chinese Academy of Sciences Institutional Repositories Grid
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CAS IR Grid
机构
地理科学与资源研究... [13]
计算技术研究所 [4]
自动化研究所 [4]
长春光学精密机械与物... [3]
遥感与数字地球研究所 [2]
过程工程研究所 [2]
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OAI收割 [34]
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期刊论文 [26]
学位论文 [5]
EI期刊论文 [1]
SCI/SSCI论文 [1]
会议论文 [1]
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2024 [1]
2023 [3]
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2020 [8]
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Towards Food Image Retrieval via Generalization-Oriented Sampling and Loss Function Design
期刊论文
OAI收割
ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS, 2024, 卷号: 20, 期号: 1, 页码: 19
作者:
Song, Jiajun
;
Li, Zhuo
;
Min, Weiqing
;
Jiang, Shuqiang
  |  
收藏
  |  
浏览/下载:8/0
  |  
提交时间:2023/12/04
Food computing
image retrieval
deep learning
Ecological restoration exacerbates the agriculture-induced water crisis in North China Region
期刊论文
OAI收割
AGRICULTURAL AND FOREST METEOROLOGY, 2023, 卷号: 331
作者:
Zhou, Yan
;
Dong, Jinwei
;
Cui, Yaoping
;
Zhao, Meng
;
Wang, Xinxin
  |  
收藏
  |  
浏览/下载:23/0
  |  
提交时间:2023/03/03
North China Region
Sustainable development
Terrestrial water storage
Ecological restoration
Evapotranspiration
Self-supervised Calorie-aware Heterogeneous Graph Networks for Food Recommendation
期刊论文
OAI收割
ACM Transactions on Multimedia Computing, Communications, and Applications, 2023, 卷号: 19, 期号: 1s, 页码: 1-23
作者:
Song, Yaguang
;
Yang, Xiaoshan
;
Xu, Changsheng
  |  
收藏
  |  
浏览/下载:9/0
  |  
提交时间:2023/06/12
Food recommendation
recipe calories
heterogeneous graph
selfsupervised learning
Rapid identification of fish species by laser-induced breakdown spectroscopy and Raman spectroscopy coupled with machine learning methods
期刊论文
OAI收割
FOOD CHEMISTRY, 2023, 卷号: 400, 页码: 9
作者:
Ren, Lihui
;
Tian, Ye
;
Yang, Xiaoying
;
Wang, Qi
;
Wang, Leshan
  |  
收藏
  |  
浏览/下载:40/0
  |  
提交时间:2023/01/12
Fish species identification
laser -induced breakdown spectroscopy (LIBS)
Raman spectroscopy
Machine learning
convolutional neural network (CNN)
Data fusion
Mapping high resolution National Soil Information Grids of China
期刊论文
OAI收割
SCIENCE BULLETIN, 2022, 卷号: 67, 期号: 3, 页码: 328-340
作者:
Liu, Feng
;
Wu, Huayong
;
Zhao, Yuguo
;
Li, Decheng
;
Yang, Jin-Ling
  |  
收藏
  |  
浏览/下载:14/0
  |  
提交时间:2022/09/21
Predictive soil mapping
Soil-landscape model
Machine learning
Depth function
Large and complex areas
Soil spatial variation
Mapping high resolution National Soil Information Grids of China
期刊论文
OAI收割
SCIENCE BULLETIN, 2022, 卷号: 67, 期号: 3, 页码: 328-340
作者:
Liu, Feng
;
Wu, Huayong
;
Zhao, Yuguo
;
Li, Decheng
;
Yang, Jin-Ling
  |  
收藏
  |  
浏览/下载:18/0
  |  
提交时间:2022/09/21
Predictive soil mapping
Soil-landscape model
Machine learning
Depth function
Large and complex areas
Soil spatial variation
TreePartNet: Neural Decomposition of Point Clouds for 3D Tree Reconstruction
期刊论文
OAI收割
ACM TRANSACTIONS ON GRAPHICS, 2021, 卷号: 40, 期号: 6, 页码: 16
作者:
Liu, Yanchao
;
Guo, Jianwei
;
Benes, Bedrich
;
Deussen, Oliver
;
Zhang, Xiaopeng
  |  
收藏
  |  
浏览/下载:26/0
  |  
提交时间:2022/02/16
3D Reconstruction
Procedural Modeling
Deep Learning
Optimization
Procedural Generation
Geometric Modeling
An Approach to High-Resolution Rice Paddy Mapping Using Time-Series Sentinel-1 SAR Data in the Mun River Basin, Thailand
期刊论文
OAI收割
REMOTE SENSING, 2020, 卷号: 12, 期号: 23, 页码: 19
作者:
Li, He
;
Fu, Dongjie
;
Huang, Chong
;
Su, Fenzhen
;
Liu, Qingsheng
  |  
收藏
  |  
浏览/下载:21/0
  |  
提交时间:2021/03/15
rice paddy
time-series
Sentinel-1
Google Earth Engine
permanent water
phenological characteristics
An Approach to High-Resolution Rice Paddy Mapping Using Time-Series Sentinel-1 SAR Data in the Mun River Basin, Thailand
期刊论文
OAI收割
REMOTE SENSING, 2020, 卷号: 12, 期号: 23, 页码: 19
作者:
Li, He
;
Fu, Dongjie
;
Huang, Chong
;
Su, Fenzhen
;
Liu, Qingsheng
  |  
收藏
  |  
浏览/下载:9/0
  |  
提交时间:2021/03/15
rice paddy
time-series
Sentinel-1
Google Earth Engine
permanent water
phenological characteristics
Few-shot Food Recognition via Multi-view Representation Learning
期刊论文
OAI收割
ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS, 2020, 卷号: 16, 期号: 3, 页码: 20
作者:
Jiang, Shuqiang
;
Min, Weiqing
;
Lyu, Yongqiang
;
Liu, Linhu
  |  
收藏
  |  
浏览/下载:17/0
  |  
提交时间:2020/12/10
Food recognition
few-shot learning
visual recognition
deep learning