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Found 25 papers, 13 papers with code
Continuous non-contact vital sign monitoring of neonates in intensive care units using RGB-D cameras
Heart rate and oxygen saturation were measured using colour and infrared signals with mean average errors (MAE) of 7. 69 bpm and 3. 37%, respectively.
STAResNet: a Network in Spacetime Algebra to solve Maxwell's PDEs
In this work we solve Maxwell's PDEs both in GA and STA employing the same ResNet architecture and dataset, to discuss the impact that the choice of the right algebra has on the accuracy of GA networks.
OpenSUN3D: 1st Workshop Challenge on Open-Vocabulary 3D Scene Understanding
This report provides an overview of the challenge hosted at the OpenSUN3D Workshop on Open-Vocabulary 3D Scene Understanding held in conjunction with ICCV 2023.
OpenIns3D: Snap and Lookup for 3D Open-vocabulary Instance Segmentation
In this work, we introduce OpenIns3D, a new 3D-input-only framework for 3D open-vocabulary scene understanding.
Ranked #1 on
Zero-shot 3D Point Cloud Classification
on ScanNetV2
3D Open-Vocabulary Instance Segmentation
3D Open-Vocabulary Object Detection
+6
CGA-PoseNet: Camera Pose Regression via a 1D-Up Approach to Conformal Geometric Algebra
This 1D-Up approach to CGA can be employed to overcome the dichotomy between translational and orientational components in camera pose regression in a compact and elegant way.
Sky-image-based solar forecasting using deep learning with multi-location data: training models locally, globally or via transfer learning?
With more and more sky image datasets open sourced in recent years, the development of accurate and reliable deep learning-based solar forecasting methods has seen a huge growth in potential.
Evaluating Self-Supervised Learning for Molecular Graph Embeddings
Graph Self-Supervised Learning (GSSL) provides a robust pathway for acquiring embeddings without expert labelling, a capability that carries profound implications for molecular graphs due to the staggering number of potential molecules and the high cost of obtaining labels.
Omnivision forecasting: combining satellite observations with sky images for improved intra-hour solar energy predictions
In this study, we integrate these two complementary points of view on the cloud cover in a single machine learning framework to improve intra-hour (up to 60-min ahead) irradiance forecasting.
SPIN: Simplifying Polar Invariance for Neural networks Application to vision-based irradiance forecasting
Translational invariance induced by pooling operations is an inherent property of convolutional neural networks, which facilitates numerous computer vision tasks such as classification.
Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence
Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses.
Pre-training Molecular Graph Representation with 3D Geometry
However, the lack of 3D information in real-world scenarios has significantly impeded the learning of geometric graph representation.
Rotaflip: A New CNN Layer for Regularization and Rotational Invariance in Medical Images
Regularization in convolutional neural networks (CNNs) is usually addressed with dropout layers.
Redesigning Fully Convolutional DenseUNets for Large Histopathology Images
The automated segmentation of cancer tissue in histopathology images can help clinicians to detect, diagnose, and analyze such disease.
ECLIPSE : Envisioning CLoud Induced Perturbations in Solar Energy
Efficient integration of solar energy into the electricity mix depends on a reliable anticipation of its intermittency.
Benchmarking of Deep Learning Irradiance Forecasting Models from Sky Images -- an in-depth Analysis
A number of industrial applications, such as smart grids, power plant operation, hybrid system management or energy trading, could benefit from improved short-term solar forecasting, addressing the intermittent energy production from solar panels.
A Temporally Consistent Image-based Sun Tracking Algorithm for Solar Energy Forecasting Applications
Improving irradiance forecasting is critical to further increase the share of solar in the energy mix.
Unsupervised Point Cloud Pre-Training via Occlusion Completion
We find that even when we construct a single pre-training dataset (from ModelNet40), this pre-training method improves accuracy across different datasets and encoders, on a wide range of downstream tasks.
Pre-Training by Completing Point Clouds
There has recently been a flurry of exciting advances in deep learning models on point clouds.
Convolutional Neural Networks applied to sky images for short-term solar irradiance forecasting
This work presents preliminary results on the application of deep Convolutional Neural Networks for 2 to 20 min irradiance forecasting using hemispherical sky images and exogenous variables.
Neural Random Subspace
The random subspace method, known as the pillar of random forests, is good at making precise and robust predictions.
The unreasonable effectiveness of the forget gate
Given the success of the gated recurrent unit, a natural question is whether all the gates of the long short-term memory (LSTM) network are necessary.
Bayesian LSTMs in medicine
The medical field stands to see significant benefits from the recent advances in deep learning.
Techniques for visualizing LSTMs applied to electrocardiograms
This paper explores four different visualization techniques for long short-term memory (LSTM) networks applied to continuous-valued time series.
Single camera pose estimation using Bayesian filtering and Kinect motion priors
This model is combined with measurements of the human head and hand positions, using recursive Bayesian estimation to incorporate temporal information.
ChESS - Quick and Robust Detection of Chess-board Features
Localization of chess-board vertices is a common task in computer vision, underpinning many applications, but relatively little work focusses on designing a specific feature detector that is fast, accurate and robust.
