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Found 33 papers, 4 papers with code
Automatic Recognition of Food Ingestion Environment from the AIM-2 Wearable Sensor
Experimental results on the collected dataset show that our proposed method for automatic ingestion environment recognition successfully addresses the challenging data imbalance problem in the dataset and achieves a promising overall classification accuracy of 96. 63%.
Food Portion Estimation via 3D Object Scaling
Image-based methods to analyze food images have alleviated the user burden and biases associated with traditional methods.
Learning to Classify New Foods Incrementally Via Compressed Exemplars
Therefore, food image classification systems should adapt to and manage data that continuously evolves.
DELTA: Decoupling Long-Tailed Online Continual Learning
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 long-tailed distributions, all while avoiding forgetting previously acquired knowledge.
Strategies to Improve Real-World Applicability of Laparoscopic Anatomy Segmentation Models
Accurate identification and localization of anatomical structures of varying size and appearance in laparoscopic imaging are necessary to leverage the potential of computer vision techniques for surgical decision support.
Probing Image Compression For Class-Incremental Learning
To this end, we introduce a new framework to incorporate image compression for continual ML including a pre-processing data compression step and an efficient compression rate/algorithm selection method.
Towards Backward-Compatible Continual Learning of Image Compression
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 compatibility, the ability to decode bitstreams encoded by the original model.
Gradient Reweighting: Towards Imbalanced Class-Incremental Learning
Class-Incremental Learning (CIL) trains a model to continually recognize new classes from non-stationary data while retaining learned knowledge.
Personalized Food Image Classification: Benchmark Datasets and New Baseline
Personalized food classification aims to address this problem by training a deep neural network using food images that reflect the consumption pattern of each individual.
An Improved Upper Bound on the Rate-Distortion Function of Images
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 image compression.
An Improved Encoder-Decoder Framework for Food Energy Estimation
Dietary assessment is essential to maintaining a healthy lifestyle.
Diffusion Model with Clustering-based Conditioning for Food Image Generation
Image-based dietary assessment serves as an efficient and accurate solution for recording and analyzing nutrition intake using eating occasion images as input.
An End-to-end Food Portion Estimation Framework Based on Shape Reconstruction from Monocular Image
Dietary assessment is a key contributor to monitoring health status.
Long-Tailed Continual Learning For Visual Food Recognition
First, as new foods appear sequentially overtime, a trained model needs to learn the new classes continuously without causing catastrophic forgetting for already learned knowledge of existing food types.
Single-Stage Heavy-Tailed Food Classification
Deep learning based food image classification has enabled more accurate nutrition content analysis for image-based dietary assessment by predicting the types of food in eating occasion images.
Conditional Synthetic Food Image Generation
Therefore, we aim to explore the capability and improve the performance of GAN methods for food image generation.
Self-Supervised Visual Representation Learning on Food Images
Food image analysis is the groundwork for image-based dietary assessment, which is the process of monitoring what kinds of food and how much energy is consumed using captured food or eating scene images.
Online Class-Incremental Learning For Real-World Food Image Classification
In this work, we explore OCIL for real-world food image classification by first introducing a probabilistic framework to simulate realistic food consumption scenarios.
Long-tailed Food Classification
Food classification serves as the basic step of image-based dietary assessment to predict the types of foods in each input image.
Simulating Personal Food Consumption Patterns using a Modified Markov Chain
Food image classification serves as the foundation of image-based dietary assessment to predict food categories.
Towards the Creation of a Nutrition and Food Group Based Image Database
Food classification is critical to the analysis of nutrients comprising foods reported in dietary assessment.
Out-Of-Distribution Detection In Unsupervised Continual Learning
Our method is evaluated on CIFAR-100 dataset by following the proposed evaluation protocol and we show improved performance compared with existing OOD detection methods under the unsupervised continual learning scenario.
Exemplar-free Online Continual Learning
Targeted for real world scenarios, online continual learning aims to learn new tasks from sequentially available data under the condition that each data is observed only once by the learner.
Online Continual Learning Via Candidates Voting
Continual learning in online scenario aims to learn a sequence of new tasks from data stream using each data only once for training, which is more realistic than in offline mode assuming data from new task are all available.
An Integrated System for Mobile Image-Based Dietary Assessment
Accurate assessment of dietary intake requires improved tools to overcome limitations of current methods including user burden and measurement error.
Improving Dietary Assessment Via Integrated Hierarchy Food Classification
Image-based dietary assessment refers to the process of determining what someone eats and how much energy and nutrients are consumed from visual data.
Online Continual Learning For Visual Food Classification
Food image classification is challenging for real-world applications since existing methods require static datasets for training and are not capable of learning from sequentially available new food images.
Unsupervised Continual Learning Via Pseudo Labels
Continual learning aims to learn new tasks incrementally using less computation and memory resources instead of retraining the model from scratch whenever new task arrives.
Towards Learning Food Portion From Monocular Images With Cross-Domain Feature Adaptation
We aim to estimate food portion size, a property that is strongly related to the presence of food object in 3D space, from single monocular images under real life setting.
An End-to-End Food Image Analysis System
Our end-to-end framework is evaluated on a real life food image dataset collected from a nutrition feeding study.
Visual Aware Hierarchy Based Food Recognition
Experimental results demonstrate that our system can significantly improve both classification and recognition performance on 4 publicly available datasets and the new VFN dataset.
Multi-Task Image-Based Dietary Assessment for Food Recognition and Portion Size Estimation
Deep learning based methods have achieved impressive results in many applications for image-based diet assessment such as food classification and food portion size estimation.
Incremental Learning In Online Scenario
Modern deep learning approaches have achieved great success in many vision applications by training a model using all available task-specific data.
