Article-Journal

A comparative study of vision–language models for food ingredient recognition and nutrient estimation
The accurate assessment of food composition is essential to understanding its nutritional and sensory properties. Traditional dietary …
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Focus more, compute less: Lightweight fruit and vegetable recognition with focus token vision transformer
Fruit and vegetable recognition is a core technology in intelligent agricultural sorting systems. Unlike general object recognition …
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SyMFood: Synergistic Multi-Modal Prompting for Fine-Grained Zero-Shot Food Detection
Fine-grained object detection in food computing is severely constrained by the vast diversity of food items and the high cost of data …
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FoodHash: Context-Aware Proxy Interaction and Fusion for Food Image Retrieval
Vision-based food image retrieval has garnered significant attention due to its potential for critical applications in dietary and …
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DPFA-net: a lightweight hybrid neural network with dual path feature aggregation for food image recognition
Food image recognition holds significant application potential in the field of computer vision. However, due to performance constraints …
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Hybrid Decoding with Co-Occurrence Awareness for Fine-Grained Food Image Segmentation
Fine-grained food image segmentation is essential for accurate dietary assessment and nutritional analysis, yet remains highly …
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LLM-Informed Global-Local Contextualization for Zero-Shot Food Detection
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Channel Grouping Vision Transformer for Lightweight Fruit and Vegetable Recognition
Recognizing fruit and vegetable is crucial for improving processing efficiency, automating harvesting, and facilitating dietary …
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Channel Grouping Vision Transformer for Lightweight Fruit and Vegetable Recognition
Lightweight Food Recognition via Aggregation Block and Feature Encoding
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Lightweight Food Recognition via Aggregation Block and Feature Encoding
基于Transformer的零样本食品图像检测
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基于Transformer的零样本食品图像检测