3 citations · 15 across the 25 of their papers we have counts for
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NutritionVerse-Synth: An Open Access Synthetically Generated 2D Food Scene Dataset for Dietary Intake Estimation
Saeejith Nair, Chi-en Amy Tai, Yuhao Chen +1
Manually tracking nutritional intake via food diaries is error-prone and burdensome. Automated computer vision techniques show promise for dietary monitoring but require large and…
FoodFusion: A Latent Diffusion Model for Realistic Food Image Generation
Olivia Markham, Yuhao Chen, Chi-en Amy Tai +1
Current state-of-the-art image generation models such as Latent Diffusion Models (LDMs) have demonstrated the capacity to produce visually striking food-related images. However, th…
Cancer-Net PCa-Gen: Synthesis of Realistic Prostate Diffusion Weighted Imaging Data via Anatomic-Conditional Controlled Latent Diffusion
Aditya Sridhar, Chi-en Amy Tai, Hayden Gunraj +2
In Canada, prostate cancer is the most common form of cancer in men and accounted for 20% of new cancer cases for this demographic in 2022. Due to recent successes in leveraging ma…
NAS-NeRF: Generative Neural Architecture Search for Neural Radiance Fields
Saeejith Nair, Yuhao Chen, Mohammad Javad Shafiee +1
Neural radiance fields (NeRFs) enable high-quality novel view synthesis, but their high computational complexity limits deployability. While existing neural-based solutions strive…
NutritionVerse: Empirical Study of Various Dietary Intake Estimation Approaches
Chi-en Amy Tai, Matthew Keller, Saeejith Nair +8
Accurate dietary intake estimation is critical for informing policies and programs to support healthy eating, as malnutrition has been directly linked to decreased quality of life.…
Continuous-Time Channel Prediction Based on Tensor Neural Ordinary Differential Equation
Mingyao Cui, Hao Jiang, Yuhao Chen +2
Channel prediction is critical to address the channel aging issue in mobile scenarios. Existing channel prediction techniques are mainly designed for discrete channel prediction, w…
Transferring Knowledge for Food Image Segmentation using Transformers and Convolutions
Grant Sinha, Krish Parmar, Hilda Azimi +4
Food image segmentation is an important task that has ubiquitous applications, such as estimating the nutritional value of a plate of food. Although machine learning models have be…
Fast GraspNeXt: A Fast Self-Attention Neural Network Architecture for Multi-task Learning in Computer Vision Tasks for Robotic Grasping on the Edge
Alexander Wong, Yifan Wu, Saad Abbasi +3
Multi-task learning has shown considerable promise for improving the performance of deep learning-driven vision systems for the purpose of robotic grasping. However, high architect…
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…
ShapeShift: Superquadric-based Object Pose Estimation for Robotic Grasping
E. Zhixuan Zeng, Yuhao Chen, Alexander Wong
Object pose estimation is a critical task in robotics for precise object manipulation. However, current techniques heavily rely on a reference 3D object, limiting their generalizab…