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20212025
most citedTransferring Knowledge for Food Image Segmentation using Transformers and Convolutions

3 citations · 15 across the 25 of their papers we have counts for

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Showing 2023 · cs.CVShow all

9 papers · 2 filters

cs.CV2023★ 1 cited

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…

cs.CV2023

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…

cs.CV2023★ 1 cited

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…

cs.CV2023

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.…

cs.CV2023★ 3 cited

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…

cs.CV2023★ 1 cited

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…

cs.CV2023

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…

cs.CV2023★ 1 cited

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…

cs.CV2023

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…