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Abstract
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.
Highlights
IntroductionThe application of nutrition monitoring using smartphones can significantly benefit from accurate food segmentation by alleviating the user from manually entering food labels and portion size for each meal
We propose GourmetNet, a single-pass, end-to-end trainable, multi-scale framework with channel and attention modules for feature refinement; The integration of channel and attention modules with waterfall spatial pyramids increases performance due to improved feature extraction combined with the multiscale waterfall approach that allows a larger FOV without requiring a separate decoder or post-processing
We presented GourmetNet, a novel, end-to-end trainable architecture for food segmentation
Summary
The application of nutrition monitoring using smartphones can significantly benefit from accurate food segmentation by alleviating the user from manually entering food labels and portion size for each meal In this context, the user takes a picture of the meal and food segmentation automatically detects each food item and provides an estimate of the portion size. The user takes a picture of the meal and food segmentation automatically detects each food item and provides an estimate of the portion size This information can be further used to assess the nutritional content of a meal and monitor the nutrition intake of an individual over a time period in order to provide recommendations for dietary improvements for health benefits.
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