DeepLab V3

DeeplabV3,即多尺度(multiple scales)分割物体,设计了串行和并行的带孔卷积模块,采用多种不同的atrous rates来获取多尺度的内容信息

语义分割
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contributed at 2020-02-15

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Method-focused categoriesData-perspectiveIntelligent computation analysis

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Quote from: https://arxiv.org/abs/1706.05587

In this work, Revisit atrous convolution, a powerful tool to explicitly adjust filter's field-of-view as well as control the resolution of feature responses computed by Deep Convolutional Neural Networks, in the application of semantic image segmentation. To handle the problem of segmenting objects at multiple scales, Design modules which employ atrous convolution in cascade or in parallel to capture multi-scale context by adopting multiple atrous rates. Furthermore, Propose to augment our previously proposed Atrous Spatial Pyramid Pooling module, which probes convolutional features at multiple scales, with image-level features encoding global context and further boost performance. Also elaborate on implementation details and share our experience on training our system. The proposed `DeepLabv3' system significantly improves over our previous DeepLab versions without DenseCRF post-processing and attains comparable performance with other state-of-art models on the PASCAL VOC 2012 semantic image segmentation benchmark.

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How to Cite

Liang-Chieh Chen , George Papandreou , Florian Schroff , Hartwig Adam (2020). DeepLab V3, Model Item, OpenGMS, https://geomodeling.njnu.edu.cn/modelItem/a80bb059-599c-4570-9674-def987226799
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Last modifier : 
Xu Kai
Last modify time : 
2020-12-18
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Initial contribute: 2020-02-15

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Last modifier : 
Xu Kai
Last modify time : 
2020-12-18
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