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论文笔记: PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
时间 2020-12-30
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论文下载 arxiv paper code Abstract 使用"Feature Extraction+Region Proposal+RoI Classification" 的结构,主要对Feature Extraction进行重新设计。因为,Region Proposal部分计算量不太大而且classification部分可以使用通用的技术(例如:Truncated SVD) 进行有效的压缩
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论文阅读--PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
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【PVANet】《PVANET:Deep but Lightweight Neural Networks for Real-time Object Detection》
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相关文章
1.
论文阅读--PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
2.
PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection - arxiv 2016.08
3.
PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
4.
【PVANet】《PVANET:Deep but Lightweight Neural Networks for Real-time Object Detection》
5.
Deep Neural Networks for Object Detection论文翻译
6.
论文阅读:Deep Neural Networks for Object Detection
7.
深度学习【51】物体检测:PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
8.
Deep Neural Networks for Object Detection
9.
【论文阅读笔记】Deep Neural Networks for Object Detection
10.
【目标检测】PVANET:Deep but Lightweight Neural Networks for Real-time Object Detection
>>更多相关文章<<