AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
Convolution Transformer Integration Network (CTNet) is proposed, a hybrid architecture that couples a residual CNN branch for local detail mining with a Transformer branch for global dependency modeling and achieves effectiveness in food recognition.
Inspired by human visual perception, where individuals first perceive an object globally and then focus on salient local details, the proposed hybrid CNN-Transformer architecture captures global representations and employs attention maps to refine task-relevant local features.
An intelligent system that automatically recognizes food items from images and provides an estimate of their calorie content using deep learning techniques, and keeps track of the total calories consumed and the remaining daily calorie allowance, helping users monitor their eating habits.
This paper presents a real-time food recognition and calorie estimation system that leverages the YOLOv8 nano object-detection architecture coupled with a curated nutritional knowledge base and outperforms prior approaches based on Faster R-CNN and SSD MobileNet on both speed and accuracy metrics.
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.
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.
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