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Found 20 papers, 9 papers with code
One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language Models
This work studies the adversarial robustness of VLMs from the novel perspective of the text prompt instead of the extensively studied model weights (frozen in this work).
A Survey of Reasoning with Foundation Models
Reasoning, a crucial ability for complex problem-solving, plays a pivotal role in various real-world settings such as negotiation, medical diagnosis, and criminal investigation.
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.
Aria-NeRF: Multimodal Egocentric View Synthesis
The construction of a NeRF-like model from an egocentric image sequence plays a pivotal role in understanding human behavior and holds diverse applications within the realms of VR/AR.
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.
CauDR: A Causality-inspired Domain Generalization Framework for Fundus-based Diabetic Retinopathy Grading
Building on this, a causality-inspired diabetic retinopathy grading framework named CauDR was developed to eliminate spurious correlations and achieve more generalizable DR diagnostics.
AROID: Improving Adversarial Robustness through Online Instance-wise Data Augmentation
This allows our method to efficiently explore a large search space for a more effective DA policy and evolve the policy as training progresses.
Generalist Vision Foundation Models for Medical Imaging: A Case Study of Segment Anything Model on Zero-Shot Medical Segmentation
In this paper, we examine the recent Segment Anything Model (SAM) on medical images, and report both quantitative and qualitative zero-shot segmentation results on nine medical image segmentation benchmarks, covering various imaging modalities, such as optical coherence tomography (OCT), magnetic resonance imaging (MRI), and computed tomography (CT), as well as different applications including dermatology, ophthalmology, and radiology.
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.
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.
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).
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.
