1 citations · 2 across the 10 of their papers we have counts for
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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…
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…