ArticleSensors (Basel, Switzerland)2021
GourmetNet: Food Segmentation Using Multi-Scale Waterfall Features with Spatial and Channel Attention.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Hybrid Decoding with Co-Occurrence Awareness for Fine-Grained Food Image Segmentation.Foods (Basel, Switzerland) · 2026Article
- Lightweight DeepLabv3+ for Semantic Food Segmentation.Foods (Basel, Switzerland) · 2025Article
- IngredSAM: Open-World Food Ingredient Segmentation via a Single Image Prompt.Journal of imaging · 2024Article
- Nutritional composition analysis in food images: an innovative Swin Transformer approach.Frontiers in nutrition · 2024Article
- mid-DeepLabv3+: A Novel Approach for Image Semantic Segmentation Applied to African Food Dietary Assessments.Sensors (Basel, Switzerland) · 2023Article
- A New CNN-Based Single-Ingredient Classification Model and Its Application in Food Image Segmentation.Journal of imaging · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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.
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Registered trials
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