Multi-Task Image-Based Dietary Assessment for Food Recognition and Portion Size Estimation
Deep learning based methods have achieved impressive results in many applications for image-based diet assessment such as food classification and food portion size estimation. However, existing methods only focus on one task at a time, making it difficult to apply in real life when multiple tasks need to be processed together. In this work, we propose an end-to-end multi-task framework that can achieve both food classification and food portion size estimation. We introduce a food image dataset collected from a nutrition study where the groundtruth food portion is provided by registered dietitians. The multi-task learning uses L2-norm based soft parameter sharing to train the classification and regression tasks simultaneously. We also propose the use of cross-domain feature adaptation together with normalization to further improve the performance of food portion size estimation. Our results outperforms the baseline methods for both classification accuracy and mean absolute error for portion estimation, which shows great potential for advancing the field of image-based dietary assessment.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationFood RecognitionGeneral ClassificationMulti-Task LearningNutritionSimilar Papers 제목 키워드 기반
Shape-Preserving Generation of Food Images for Automatic Dietary Assessment
Traditional dietary assessment methods heavily rely on self-reporting, which is time-consuming and prone to bias. Recent advancements in Artificial Intelligence (AI) have revealed new possibilities for dietary assessment…
Image GenerationDeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment
Worldwide, in 2014, more than 1.9 billion adults, 18 years and older, were overweight. Of these, over 600 million were obese. Accurately documenting dietary caloric intake is crucial to manage weight loss, but also prese…
Cloud ComputingFine-Grained Image RecognitionA review on vision-based analysis for automatic dietary assessment
Background: Maintaining a healthy diet is vital to avoid health-related issues, e.g., undernutrition, obesity and many non-communicable diseases. An indispensable part of the health diet is dietary assessment. Traditiona…
Food RecognitionNutritionSaliency-Aware Class-Agnostic Food Image Segmentation
Advances in image-based dietary assessment methods have allowed nutrition professionals and researchers to improve the accuracy of dietary assessment, where images of food consumed are captured using smartphones or weara…
Image SegmentationNutritionSemantic SegmentationDietary Assessment with Multimodal ChatGPT: A Systematic Analysis
Conventional approaches to dietary assessment are primarily grounded in self-reporting methods or structured interviews conducted under the supervision of dietitians. These methods, however, are often subjective, potenti…
Image CaptioningScene Understanding