3 citations · 4 across the 5 of their papers we have counts for
5 papers
MetaFood CVPR 2024 Challenge on Physically Informed 3D Food Reconstruction: Methods and Results
Jiangpeng He, Yuhao Chen, Gautham Vinod +16
The increasing interest in computer vision applications for nutrition and dietary monitoring has led to the development of advanced 3D reconstruction techniques for food items. How…
NutritionVerse-Thin: An Optimized Strategy for Enabling Improved Rendering of 3D Thin Food Models
Chi-en Amy Tai, Jason Li, Sriram Kumar +4
With the growth in capabilities of generative models, there has been growing interest in using photo-realistic renders of common 3D food items to improve downstream tasks such as f…
NutritionVerse-3D: A 3D Food Model Dataset for Nutritional Intake Estimation
Chi-en Amy Tai, Matthew Keller, Mattie Kerrigan +4
77% of adults over 50 want to age in place today, presenting a major challenge to ensuring adequate nutritional intake. It has been reported that one in four older adults that are…
Towards Trustworthy Healthcare AI: Attention-Based Feature Learning for COVID-19 Screening With Chest Radiography
Kai Ma, Pengcheng Xi, Karim Habashy +3
Building AI models with trustworthiness is important especially in regulated areas such as healthcare. In tackling COVID-19, previous work uses convolutional neural networks as the…
Performance or Trust? Why Not Both. Deep AUC Maximization with Self-Supervised Learning for COVID-19 Chest X-ray Classifications
Siyuan He, Pengcheng Xi, Ashkan Ebadi +2
Effective representation learning is the key in improving model performance for medical image analysis. In training deep learning models, a compromise often must be made between pe…