1 citations · 2 across the 10 of their papers we have counts for
10 papers
When does fusing hand-crafted knowledge with learned representations pay? A cost-normalized benchmark of stacking, substitution, and interference
Ahmad AlMughrabi, Albert Clop, Benjamin Busam +2
Fusing prior knowledge with data-driven learning is attractive where data is scarce, yet no controlled account says when it helps, is redundant, or harms. We benchmark one fixed ha…
PerBite: A Curated Diagnostic Workflow for Bite-Aware Food Volume Estimation
Ahmad AlMughrabi, Farid Al-Areqi, David Fernández Gómez +4
Can a visually plausible food mesh be trusted to estimate the volume of consumed food? \method investigates this question using selected paired before- and after-consumption states…
BenchSeg: A Large-Scale Dataset and Benchmark for Multi-View Food Video Segmentation
Ahmad AlMughrabi, Guillermo Rivo, Carlos Jiménez-Farfán +6
Food image segmentation is a critical task for dietary analysis, enabling accurate estimation of food volume and nutrients. However, current methods suffer from limited multi-view…
VolTex: Food Volume Estimation using Text-Guided Segmentation and Neural Surface Reconstruction
Ahmad AlMughrabi, Umair Haroon, Ricardo Marques +1
Accurate food volume estimation is crucial for dietary monitoring, medical nutrition management, and food intake analysis. Existing 3D Food Volume estimation methods accurately com…
VolE: A Point-cloud Framework for Food 3D Reconstruction and Volume Estimation
Umair Haroon, Ahmad AlMughrabi, Thanasis Zoumpekas +2
Accurate food volume estimation is crucial for medical nutrition management and health monitoring applications, but current food volume estimation methods are often limited by mono…
MVSBoost: An Efficient Point Cloud-based 3D Reconstruction
Umair Haroon, Ahmad AlMughrabi, Ricardo Marques +1
Efficient and accurate 3D reconstruction is crucial for various applications, including augmented and virtual reality, medical imaging, and cinematic special effects. While traditi…
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…
MomentsNeRF: Leveraging Orthogonal Moments for Few-Shot Neural Rendering
Ahmad AlMughrabi, Ricardo Marques, Petia Radeva
We propose MomentsNeRF, a novel framework for one- and few-shot neural rendering that predicts a neural representation of a 3D scene using Orthogonal Moments. Our architecture offe…
FoodMem: Near Real-time and Precise Food Video Segmentation
Ahmad AlMughrabi, Adrián Galán, Ricardo Marques +1
Food segmentation, including in videos, is vital for addressing real-world health, agriculture, and food biotechnology issues. Current limitations lead to inaccurate nutritional an…
VolETA: One- and Few-shot Food Volume Estimation
Ahmad AlMughrabi, Umair Haroon, Ricardo Marques +1
Accurate food volume estimation is essential for dietary assessment, nutritional tracking, and portion control applications. We present VolETA, a sophisticated methodology for esti…