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NutritionVerse: Empirical Study of Various Dietary Intake Estimation Approaches
Recent work has focused on using computer vision and machine learning to automatically estimate dietary intake from food images, but the lack of comprehensive datasets with diverse viewpoints, modalities and food annotations hinders the accuracy and realism of such methods.
Transferring Knowledge for Food Image Segmentation using Transformers and Convolutions
To address these issues, two models are trained and compared, one based on convolutional neural networks and the other on Bidirectional Encoder representation for Image Transformers (BEiT).
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