GourmetNet: Food Segmentation Using Multi-Scale Waterfall Features with Spatial and Channel Attention

We propose GourmetNet, a single-pass, end-to-end trainable network for food segmentation that achieves state-of-the-art performance. Food segmentation is an important problem as the first step for nutrition monitoring, food volume and calorie estimation. Our novel architecture incorporates both chan...

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Autores principales:	Udit Sharma, Bruno Artacho, Andreas Savakis
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	semantic segmentation food segmentation multi-scale features spatial attention channel attention Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/1115306637554b4f9acc4faeacaeaada
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id	oai:doaj.org-article:1115306637554b4f9acc4faeacaeaada
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spelling	oai:doaj.org-article:1115306637554b4f9acc4faeacaeaada2021-11-25T18:56:57ZGourmetNet: Food Segmentation Using Multi-Scale Waterfall Features with Spatial and Channel Attention10.3390/s212275041424-8220https://doaj.org/article/1115306637554b4f9acc4faeacaeaada2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7504https://doaj.org/toc/1424-8220We propose GourmetNet, a single-pass, end-to-end trainable network for food segmentation that achieves state-of-the-art performance. Food segmentation is an important problem as the first step for nutrition monitoring, food volume and calorie estimation. Our novel architecture incorporates both channel attention and spatial attention information in an expanded multi-scale feature representation using our advanced Waterfall Atrous Spatial Pooling module. GourmetNet refines the feature extraction process by merging features from multiple levels of the backbone through the two attention modules. The refined features are processed with the advanced multi-scale waterfall module that combines the benefits of cascade filtering and pyramid representations without requiring a separate decoder or post-processing. Our experiments on two food datasets show that GourmetNet significantly outperforms existing current state-of-the-art methods.Udit SharmaBruno ArtachoAndreas SavakisMDPI AGarticlesemantic segmentationfood segmentationmulti-scale featuresspatial attentionchannel attentionChemical technologyTP1-1185ENSensors, Vol 21, Iss 7504, p 7504 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	semantic segmentation food segmentation multi-scale features spatial attention channel attention Chemical technology TP1-1185
spellingShingle	semantic segmentation food segmentation multi-scale features spatial attention channel attention Chemical technology TP1-1185 Udit Sharma Bruno Artacho Andreas Savakis GourmetNet: Food Segmentation Using Multi-Scale Waterfall Features with Spatial and Channel Attention
description	We propose GourmetNet, a single-pass, end-to-end trainable network for food segmentation that achieves state-of-the-art performance. Food segmentation is an important problem as the first step for nutrition monitoring, food volume and calorie estimation. Our novel architecture incorporates both channel attention and spatial attention information in an expanded multi-scale feature representation using our advanced Waterfall Atrous Spatial Pooling module. GourmetNet refines the feature extraction process by merging features from multiple levels of the backbone through the two attention modules. The refined features are processed with the advanced multi-scale waterfall module that combines the benefits of cascade filtering and pyramid representations without requiring a separate decoder or post-processing. Our experiments on two food datasets show that GourmetNet significantly outperforms existing current state-of-the-art methods.
format	article
author	Udit Sharma Bruno Artacho Andreas Savakis
author_facet	Udit Sharma Bruno Artacho Andreas Savakis
author_sort	Udit Sharma
title	GourmetNet: Food Segmentation Using Multi-Scale Waterfall Features with Spatial and Channel Attention
title_short	GourmetNet: Food Segmentation Using Multi-Scale Waterfall Features with Spatial and Channel Attention
title_full	GourmetNet: Food Segmentation Using Multi-Scale Waterfall Features with Spatial and Channel Attention
title_fullStr	GourmetNet: Food Segmentation Using Multi-Scale Waterfall Features with Spatial and Channel Attention
title_full_unstemmed	GourmetNet: Food Segmentation Using Multi-Scale Waterfall Features with Spatial and Channel Attention
title_sort	gourmetnet: food segmentation using multi-scale waterfall features with spatial and channel attention
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/1115306637554b4f9acc4faeacaeaada
work_keys_str_mv	AT uditsharma gourmetnetfoodsegmentationusingmultiscalewaterfallfeatureswithspatialandchannelattention AT brunoartacho gourmetnetfoodsegmentationusingmultiscalewaterfallfeatureswithspatialandchannelattention AT andreassavakis gourmetnetfoodsegmentationusingmultiscalewaterfallfeatureswithspatialandchannelattention
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GourmetNet: Food Segmentation Using Multi-Scale Waterfall Features with Spatial and Channel Attention

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