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Found 19 papers, 7 papers with code
Dietary Assessment with Multimodal ChatGPT: A Systematic Analysis
Although artificial intelligence (AI)-based solutions have been devised to automate the dietary assessment process, these prior AI methodologies encounter challenges in their ability to generalize across a diverse range of food types, dietary behaviors, and cultural contexts.
VisionFM: a Multi-Modal Multi-Task Vision Foundation Model for Generalist Ophthalmic Artificial Intelligence
To be commensurate with this capacity, in addition to the real data used for pre-training, we also generated and leveraged synthetic ophthalmic imaging data.
IoT Federated Blockchain Learning at the Edge
The system is designed for three paradigms: 1) Training neural networks on IoT devices to allow for collaborative training of a shared model whilst decoupling the learning from the dataset to ensure privacy.
Large AI Models in Health Informatics: Applications, Challenges, and the Future
Large AI models, or foundation models, are models recently emerging with massive scales both parameter-wise and data-wise, the magnitudes of which can reach beyond billions.
EVEN: An Event-Based Framework for Monocular Depth Estimation at Adverse Night Conditions
Comprehensive experiments have been conducted, and the impact of different adverse weather combinations on the performance of framework has also been investigated.
MenuAI: Restaurant Food Recommendation System via a Transformer-based Deep Learning Model
The promising results, with accuracy ranging from 77. 2% to 99. 5%, have demonstrated the great potential of LTR model in addressing food recommendation problems.
Revisiting Self-Supervised Contrastive Learning for Facial Expression Recognition
The success of most advanced facial expression recognition works relies heavily on large-scale annotated datasets.
Clustering Egocentric Images in Passive Dietary Monitoring with Self-Supervised Learning
By clustering images into separate events, annotators and dietitians can examine and analyze the data more efficiently and facilitate the subsequent dietary assessment processes.
Tackling Long-Tailed Category Distribution Under Domain Shifts
Two new datasets were proposed for this problem, named AWA2-LTS and ImageNet-LTS.
Mining Discriminative Food Regions for Accurate Food Recognition
Taking inspiration from Adversarial Erasing, a strategy that progressively discovers discriminative object regions for weakly supervised semantic segmentation, we propose a novel network architecture in which a primary network maintains the base accuracy of classifying an input image, an auxiliary network adversarially mines discriminative food regions, and a region network classifies the resulting mined regions.
Deep Reinforcement Learning Based Semi-Autonomous Control for Robotic Surgery
In recent decades, the tremendous benefits surgical robots have brought to surgeons and patients have been witnessed.
Egocentric Human Trajectory Forecasting with a Wearable Camera and Multi-Modal Fusion
To this end, a novel egocentric human trajectory forecasting dataset was constructed, containing real trajectories of people navigating in crowded spaces wearing a camera, as well as extracted rich contextual data.
Occlusion-Invariant Rotation-Equivariant Semi-Supervised Depth Based Cross-View Gait Pose Estimation
It can generalize well on the real-world data from all the other unseen views.
TransAction: ICL-SJTU Submission to EPIC-Kitchens Action Anticipation Challenge 2021
In this report, the technical details of our submission to the EPIC-Kitchens Action Anticipation Challenge 2021 are given.
Egocentric Image Captioning for Privacy-Preserved Passive Dietary Intake Monitoring
In this paper, we propose a privacy-preserved secure solution (i. e., egocentric image captioning) for dietary assessment with passive monitoring, which unifies food recognition, volume estimation, and scene understanding.
An Intelligent Passive Food Intake Assessment System with Egocentric Cameras
Malnutrition is a major public health concern in low-and-middle-income countries (LMICs).
Surgical Gesture Recognition Based on Bidirectional Multi-Layer Independently RNN with Explainable Spatial Feature Extraction
It can provide explainable results by showing the regions of the surgical images that have a strong relationship with the surgical gesture classification results.
Indoor Future Person Localization from an Egocentric Wearable Camera
Accurate prediction of future person location and movement trajectory from an egocentric wearable camera can benefit a wide range of applications, such as assisting visually impaired people in navigation, and the development of mobility assistance for people with disability.
Real-time Surgical Environment Enhancement for Robot-Assisted Minimally Invasive Surgery Based on Super-Resolution
In Robot-Assisted Minimally Invasive Surgery (RAMIS), a camera assistant is normally required to control the position and zooming ratio of the laparoscope, following the surgeon's instructions.
