18 citations · 46 across the 32 of their papers we have counts for
37 papers · 1 filter
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…