.png){kind=icon}
返回
Efficient High-Resolution Deep Learning: A Survey
.png){kind=icon}
OA
AI
DOI：10.1145/3645107.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Cameras in modern devices such as smartphones, satellites and medical equipment are capable of capturing very high resolution images and videos. Such high-resolution data often need to be processed by deep learning models for cancer detection, automated road navigation, weather prediction, surveillance, optimizing agricultural processes and many other applications. Using high-resolution images and videos as direct inputs for deep learning models creates many challenges due to their high number of parameters, computation cost, inference latency and GPU memory consumption. Simple approaches such as resizing the images to a lower resolution are common in the literature, however, they typically significantly decrease accuracy. Several works in the literature propose better alternatives in order to deal with the challenges of high-resolution data and improve accuracy and speed while complying with hardware limitations and time restrictions. This survey describes such efficient high-resolution deep learning methods, summarizes real-world applications of high-resolution deep learning, and provides comprehensive information about available high-resolution datasets.
Keyword:
High-resolution deep learning
efficient deep learning
vision transformer
期刊
IF:
28
论文数:
2.4K
被引数:
3.5W
机构
引用论文
分享收藏Graph Embedded Convolutional Neural Networks in Human Crowd Detection for Drone Flight Safety图嵌入式卷积神经网络在无人机飞行安全人群检测中的应用
PREAI
分享收藏分享收藏INbreast: Toward a Full-field Digital Mammographic Database乳房: 迈向全视野数字乳腺摄影数据库.png)
ACADEMIC RADIOLOGY
OAAI
ACADEMIC RADIOLOGYIF3.9
OAAI分享收藏Determinants of quality of life in patients with fibromyalgia: A structural equation modeling approach纤维肌痛患者生活质量的决定因素: 结构方程建模方法PLOS ONEOAAI
PLOS ONEIF0
OAAI分享收藏
查看更多内容