20 citations · 24 across the 13 of their papers we have counts for
14 papers
Reference-Free Food Volume Estimation from Egocentric Stereo Video
Vivan Kumar, Gautham Vinod, Fengqing Zhu
Current stereo vision-based approaches for food portion estimation are hindered by the user burden of using a mobile phone to capture multiple images or placing a reference object…
Mitigating Performance Discrepancy in Cross-Domain 3D Class-Incremental Learning
Jinge Ma, Gautham Vinod, Bruce Coburn +3
3D perception plays a crucial role in real-world applications such as autonomous driving, robotics, and AR/VR. In practical scenarios, 3D perception models need to continually adap…
Open-KNEAD: Knowledge-grounded Nutrition Estimation via Agentic Decomposition
Bruce Coburn, Jingbo Yue, Jinge Ma +3
Multimodal Large Language Models (MLLMs) are increasingly used for dietary assessment from meal images, where retrieval-augmented grounding was shown to sharpen nutrition estimates…
Not Your Stereo-Typical Estimator: Combining Vision and Language for Volume Perception
Gautham Vinod, Bruce Coburn, Siddeshwar Raghavan +1
Accurate volume estimation of objects from visual data is a long-standing challenge in computer vision with significant applications in robotics, logistics, and smart health. Exist…
DietDelta: A Vision-Language Approach for Dietary Assessment via Before-and-After Images
Gautham Vinod, Siddeshwar Raghavan, Bruce Coburn +1
Accurate dietary assessment is critical for precision nutrition, yet most image-based methods rely on a single pre-consumption image and provide only coarse, meal-level estimates.…
Can You Hear, Localize, and Segment Continually? An Exemplar-Free Continual Learning Benchmark for Audio-Visual Segmentation
Siddeshwar Raghavan, Gautham Vinod, Bruce Coburn +1
Audio-Visual Segmentation (AVS) aims to produce pixel-level masks of sound producing objects in videos, by jointly learning from audio and visual signals. However, real-world envir…
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…
Food Portion Estimation: From Pixels to Calories
Gautham Vinod, Fengqing Zhu
Reliance on images for dietary assessment is an important strategy to accurately and conveniently monitor an individual's health, making it a vital mechanism in the prevention and…
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…
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…
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…
Food Portion Estimation via 3D Object Scaling
Gautham Vinod, Jiangpeng He, Zeman Shao +1
Image-based methods to analyze food images have alleviated the user burden and biases associated with traditional methods. However, accurate portion estimation remains a major chal…
An End-to-end Food Portion Estimation Framework Based on Shape Reconstruction from Monocular Image
Zeman Shao, Gautham Vinod, Jiangpeng He +1
Dietary assessment is a key contributor to monitoring health status. Existing self-report methods are tedious and time-consuming with substantial biases and errors. Image-based foo…
Image Based Food Energy Estimation With Depth Domain Adaptation
Gautham Vinod, Zeman Shao, Fengqing Zhu
Assessment of dietary intake has primarily relied on self-report instruments, which are prone to measurement errors. Dietary assessment methods have increasingly incorporated techn…