From the 2 of 8 linked papers with an AI index.
1 citations · 1 across the 5 of their papers we have counts for
7 papers · 1 filter
Fine-Grained Food Image Understanding via Target-Aware Data Alignment
Jui-Feng Chi, Wei-Lun Chu, Bruce Coburn +2
The paper introduces a data-centric approach that selects and refines web-collected image‑caption pairs to better train CLIP‑style vision‑language models for fine‑grained food reco…
Open-KNEAD: Knowledge-grounded Nutrition Estimation via Agentic Decomposition
Bruce Coburn, Jingbo Yue, Jinge Ma +3
The paper presents Open-KNEAD, a training-free, locally run agentic system that breaks down meal images into individual food items, grounds each to a nutrition database, and improv…
MFP3D: Monocular Food Portion Estimation Leveraging 3D Point Clouds
Jinge Ma, Xiaoyan Zhang, Gautham Vinod +3
Food portion estimation is crucial for monitoring health and tracking dietary intake. Image-based dietary assessment, which involves analyzing eating occasion images using computer…
Implicit-Scale 3D Reconstruction for Multi-Food Volume Estimation from Monocular Images
Yuhao Chen, Gautham Vinod, Siddeshwar Raghavan +5
We present Implicit-Scale 3D Reconstruction from Monocular Multi-Food Images, a benchmark dataset designed to advance geometry-based food portion estimation in realistic dining sce…
Robust3D-CIL: Robust Class-Incremental Learning for 3D Perception
Jinge Ma, Jiangpeng He, Fengqing Zhu
3D perception plays a crucial role in real-world applications such as autonomous driving, robotics, and AR/VR. In practical scenarios, 3D perception models must continuously adapt…
MetaFood3D: 3D Food Dataset with Nutrition Values
Yuhao Chen, Jiangpeng He, Gautham Vinod +11
Food computing is both important and challenging in computer vision (CV). It significantly contributes to the development of CV algorithms due to its frequent presence in datasets…