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Found 19 papers, 6 papers with code
eProduct: A Million-Scale Visual Search Benchmark to Address Product Recognition Challenges
This motivated creation of eProduct, a dataset consisting of 2. 5 million product images towards accelerating development in the areas of self-supervised learning, weakly-supervised learning, and multimodal learning, for fine-grained recognition.
De Re Updates
In this paper, we propose a lightweight yet powerful dynamic epistemic logic that captures not only the distinction between de dicto and de re knowledge but also the distinction between de dicto and de re updates.
Deep Transform and Metric Learning Networks
Based on its great successes in inference and denosing tasks, Dictionary Learning (DL) and its related sparse optimization formulations have garnered a lot of research interest.
CovidCare: Transferring Knowledge from Existing EMR to Emerging Epidemic for Interpretable Prognosis
Due to the characteristics of COVID-19, the epidemic develops rapidly and overwhelms health service systems worldwide.
Deep Transform and Metric Learning Network: Wedding Deep Dictionary Learning and Neural Networks
On account of its many successes in inference tasks and denoising applications, Dictionary Learning (DL) and its related sparse optimization problems have garnered a lot of research interest.
StageNet: Stage-Aware Neural Networks for Health Risk Prediction
Compared to the best baseline model, StageNet achieves up to 12% higher AUPRC for risk prediction task on two real-world patient datasets.
AdaCare: Explainable Clinical Health Status Representation Learning via Scale-Adaptive Feature Extraction and Recalibration
It also models the correlation between clinical features to enhance the ones which strongly indicate the health status and thus can maintain a state-of-the-art performance in terms of prediction accuracy while providing qualitative interpretability.
ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context
Predicting the patient's clinical outcome from the historical electronic medical records (EMR) is a fundamental research problem in medical informatics.
Joint Concept Matching based Learning for Zero-Shot Recognition
Zero-shot learning (ZSL) which aims to recognize unseen object classes by only training on seen object classes, has increasingly been of great interest in Machine Learning, and has registered with some successes.
MUSEFood: Multi-sensor-based Food Volume Estimation on Smartphones
Furthermore, MUSEFood uses the microphone and the speaker to accurately measure the vertical distance from the camera to the food in a noisy environment, thus scaling the size of food in the image to its actual size.
Analysis Dictionary Learning: An Efficient and Discriminative Solution
Discriminative Dictionary Learning (DL) methods have been widely advocated for image classification problems.
Analysis Dictionary Learning based Classification: Structure for Robustness
A consensus structured analysis dictionary and a global classifier are jointly learned in the distributed approach to safeguard the discriminative power and the efficiency of classification.
Structured Analysis Dictionary Learning for Image Classification
We propose a computationally efficient and high-performance classification algorithm by incorporating class structural information in analysis dictionary learning.
Self-Supervised Monocular Image Depth Learning and Confidence Estimation
Convolutional Neural Networks (CNNs) need large amounts of data with ground truth annotation, which is a challenging problem that has limited the development and fast deployment of CNNs for many computer vision tasks.
Context-Aware Mixed Reality: A Framework for Ubiquitous Interaction
Mixed Reality (MR) is a powerful interactive technology that yields new types of user experience.
Recent Developments and Future Challenges in Medical Mixed Reality
Mixed Reality (MR) is of increasing interest within technology-driven modern medicine but is not yet used in everyday practice.
Real-time Geometry-Aware Augmented Reality in Minimally Invasive Surgery
The potential of Augmented Reality (AR) technology to assist minimally invasive surgeries (MIS) lies in its computational performance and accuracy in dealing with challenging MIS scenes.
Semantic Augmented Reality Environment with Material-Aware Physical Interactions
In Augmented Reality (AR) environment, realistic interactions between the virtual and real objects play a crucial role in user experience.
Augmented Reality for Depth Cues in Monocular Minimally Invasive Surgery
In vivo laparoscopic videos used in the tests have demonstrated the robustness and accuracy of our proposed framework on both camera tracking and surface reconstruction, illustrating the potential of our algorithm for depth augmentation and depth-corrected augmented reality in MIS with monocular endoscopes.
Simultaneous Localization and Mapping
Surface Reconstruction
