AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This work designs a dual-backbone feature extraction architecture for food nutrition estimation, and introduces the Adaptive Spatial Feature Fusion (ASFF), which fuse ingredient semantic features with visual features through Softmax weighted interaction to enhance feature fusion.
Nutrition estimation is a fundamental task in consumer diet tracking, clinical dietetics, chronic disease management, sports and hospital nutrition, and broader food computing systems. The existing approaches have progressed along two largely separate axes, vision models that rely on calibrated RGB-depth captures and i...
A multimodal transformer-based framework called ViT-FoodNA that integrates these tasks into one end-to-end framework and uses the self-attention mechanism to learn fine-grained intermodal dependencies, which enhances its resistance to visual ambiguity and missing ingredients in a list.
Imagine having a personalized nutrition plan that caters to your unique dietary needs based on your age and gender. Such a system could revolutionize the way we approach health and wellness. A key component of this vision is the accurate classification of age and gender from facial images, which can be leveraged to pro...
Food image classification is an important task in computer vision because it can provide an image-level recognition component for potential applications in dietary assessment, nutritional monitoring, food recommendation, and AI-assisted digital health services. However, Indonesian food classification remains challengin...
The VAE-based FRS (VAEFRS) system achieved optimization through its training process, which used a Conditional Tabular Generative Adversarial Network (CTGAN)-augmented training manifold to obtain full coverage of high-dimensional features while preventing overfitting issues.
A structured literature review of CNN-based food image classification studies published between 2020 and 2025 identifies persistent gaps in statistical validation, class imbalance treatment, explainability, reproducibility, public code availability and deployment-oriented evaluation.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.