18 citations · 73 across the 41 of their papers we have counts for
11 papers · 1 filter
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…
FMiFood: Multi-modal Contrastive Learning for Food Image Classification
Xinyue Pan, Jiangpeng He, Fengqing Zhu
Food image classification is the fundamental step in image-based dietary assessment, which aims to estimate participants' nutrient intake from eating occasion images. A common chal…
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…
Strategies to Improve Real-World Applicability of Laparoscopic Anatomy Segmentation Models
Fiona R. Kolbinger, Jiangpeng He, Jinge Ma +1
Accurate identification and localization of anatomical structures of varying size and appearance in laparoscopic imaging are necessary to leverage the potential of computer vision…
Learning to Classify New Foods Incrementally Via Compressed Exemplars
Justin Yang, Zhihao Duan, Jiangpeng He +1
Food image classification systems play a crucial role in health monitoring and diet tracking through image-based dietary assessment techniques. However, existing food recognition s…
DELTA: Decoupling Long-Tailed Online Continual Learning
Siddeshwar Raghavan, Jiangpeng He, Fengqing Zhu
A significant challenge in achieving ubiquitous Artificial Intelligence is the limited ability of models to rapidly learn new information in real-world scenarios where data follows…
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…
Gradient Reweighting: Towards Imbalanced Class-Incremental Learning
Jiangpeng He, Fengqing Zhu
Class-Incremental Learning (CIL) trains a model to continually recognize new classes from non-stationary data while retaining learned knowledge. A major challenge of CIL arises whe…
Probing Image Compression For Class-Incremental Learning
Justin Yang, Zhihao Duan, Andrew Peng +3
Image compression emerges as a pivotal tool in the efficient handling and transmission of digital images. Its ability to substantially reduce file size not only facilitates enhance…
Towards Backward-Compatible Continual Learning of Image Compression
Zhihao Duan, Ming Lu, Justin Yang +3
This paper explores the possibility of extending the capability of pre-trained neural image compressors (e.g., adapting to new data or target bitrates) without breaking backward co…