11 citations · 16 across the 5 of their papers we have counts for
6 papers
Training-Free Text-to-Image Compositional Food Generation via Prompt Grafting
Xinyue Pan, Yuhao Chen, Fengqing Zhu
Real-world meal images often contain multiple food items, making reliable compositional food image generation important for applications such as image-based dietary assessment, whe…
Food Image Generation on Multi-Noun Categories
Xinyue Pan, Yuhao Chen, Jiangpeng He +1
Generating realistic food images for categories with multiple nouns is surprisingly challenging. For instance, the prompt "egg noodle" may result in images that incorrectly contain…
FMiFood: Multi-modal Contrastive Learning for Food Image Classification
Xinyue Pan, Jiangpeng He, Fengqing Zhu
Food image classification is the fundamental step in image-based dietary assessment, which aims to estimate participants' nutrient intake from eating occasion images. A common chal…
Personalized Food Image Classification: Benchmark Datasets and New Baseline
Xinyue Pan, Jiangpeng He, Fengqing Zhu
Food image classification is a fundamental step of image-based dietary assessment, enabling automated nutrient analysis from food images. Many current methods employ deep neural ne…
Muti-Stage Hierarchical Food Classification
Xinyue Pan, Jiangpeng He, Fengqing Zhu
Food image classification serves as a fundamental and critical step in image-based dietary assessment, facilitating nutrient intake analysis from captured food images. However, exi…
Simulating Personal Food Consumption Patterns using a Modified Markov Chain
Xinyue Pan, Jiangpeng He, Andrew Peng +1
Food image classification serves as the foundation of image-based dietary assessment to predict food categories. Since there are many different food classes in real life, conventio…