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About Deeplab: Semantic Image Segmentation With Deep Convolutional Nets, Atrous Convolution, and Fully Connected Crfs
This document proposes DeepLab, a system that improves semantic image segmentation with deep convolutional networks. It addresses three key challenges: 1) reduced feature resolution from downsampling, 2) objects existing at multiple scales, and 3) reduced localization from CNN invariance. To address these, DeepLab uses atrous convolution to compute higher resolution feature maps without increasing parameters. It also uses atrous spatial pyramid pooling to capture objects and context at multiple scales efficiently. Finally, it combines CNN and CRF outputs to improve localization. Experimental results show DeepLab achieves state-of-the-art performance on four semantic segmentation datasets.
Detailed Information
| Author: | ['Designer Tees'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 14 |
| Language: | English |
| Format: | |
| Price: | FREE |
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