Proceedings of the Annual Conference of JSAI
Online ISSN : 2758-7347
35th (2021)
Session ID : 3D2-OS-12b-03
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Free Configurational Atrous Convolution for Semantic Segmentation
*Ryoma OKAMOTOAkira TERAUCHINaoki MORIMakoto OKADA
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Abstract

In recent years, semantic segmentation by DeepLabv3+ has attracted much attention. One of the features of this model is atrous convolution, which adjusts the convolutional range of filter (field-of-view of filter) by dilating its convolutional position (viewpoints of filter). However, previous atrous convolution considers only adjusting field-of-view of filter and does not consider adjusting viewpoints of filter. In this paper, we propose free configurational atrous convolution, which is an extension of the viewpoint placement method in previous atrous convolution, and semantic segmentation based on this method. In the proposed method, we first partition viewpoints in the 3 × 3 convolutional filter into two groups. Next, we applied atrous convolution with different rates to the filters. Then, by adding the outputs, we realized the convolution with the adjustment of field-of-view of filter and viewpoints of filter. The effectiveness of the proposed method is confirmed by computer simulations taking the benchmark dataset of semantic segmentation as an example.

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© 2021 The Japanese Society for Artificial Intelligence
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