18 citations · 73 across the 41 of their papers we have counts for
44 papers
V-Nutri: Dish-Level Nutrition Estimation from Egocentric Cooking Videos
Chengkun Yue, Chuanzhi Xu, Jiangpeng He
Nutrition estimation of meals from visual data is an important problem for dietary monitoring and computational health, but existing approaches largely rely on single images of the…
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…
PANDA -- Patch And Distribution-Aware Augmentation for Long-Tailed Exemplar-Free Continual Learning
Siddeshwar Raghavan, Jiangpeng He, Fengqing Zhu
Exemplar-Free Continual Learning (EFCL) restricts the storage of previous task data and is highly susceptible to catastrophic forgetting. While pre-trained models (PTMs) are increa…
Food Image Generation on Multi-Noun Categories
Xinyue Pan, Yuhao Chen, Jiangpeng He +1
Generating realistic food images for categories with multiple nouns is surprisingly challenging. For instance, the prompt "egg noodle" may result in images that incorrectly contain…
Comprehensive Evaluation of Large Multimodal Models for Nutrition Analysis: A New Benchmark Enriched with Contextual Metadata
Bruce Coburn, Jiangpeng He, Megan E. Rollo +3
Large Multimodal Models (LMMs) are increasingly applied to meal images for nutrition analysis. However, existing work primarily evaluates proprietary models, such as GPT-4. This le…
CL-LoRA: Continual Low-Rank Adaptation for Rehearsal-Free Class-Incremental Learning
Jiangpeng He, Zhihao Duan, Fengqing Zhu
Class-Incremental Learning (CIL) aims to learn new classes sequentially while retaining the knowledge of previously learned classes. Recently, pre-trained models (PTMs) combined wi…
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…
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…
An Improved Encoder-Decoder Framework for Food Energy Estimation
Jack Ma, Jiangpeng He, Fengqing Zhu
Dietary assessment is essential to maintaining a healthy lifestyle. Automatic image-based dietary assessment is a growing field of research due to the increasing prevalence of imag…
Personalized Food Image Classification: Benchmark Datasets and New Baseline
Xinyue Pan, Jiangpeng He, Fengqing Zhu
Food image classification is a fundamental step of image-based dietary assessment, enabling automated nutrient analysis from food images. Many current methods employ deep neural ne…
An Improved Upper Bound on the Rate-Distortion Function of Images
Zhihao Duan, Jack Ma, Jiangpeng He +1
Recent work has shown that Variational Autoencoders (VAEs) can be used to upper-bound the information rate-distortion (R-D) function of images, i.e., the fundamental limit of lossy…
Muti-Stage Hierarchical Food Classification
Xinyue Pan, Jiangpeng He, Fengqing Zhu
Food image classification serves as a fundamental and critical step in image-based dietary assessment, facilitating nutrient intake analysis from captured food images. However, exi…
Diffusion Model with Clustering-based Conditioning for Food Image Generation
Yue Han, Jiangpeng He, Mridul Gupta +2
Image-based dietary assessment serves as an efficient and accurate solution for recording and analyzing nutrition intake using eating occasion images as input. Deep learning-based…
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…