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Found 27 papers, 2 papers with code
Cross-device Federated Learning for Mobile Health Diagnostics: A First Study on COVID-19 Detection
In this paper, we propose FedLoss, a novel cross-device FL framework for health diagnostics.
Indoor simultaneous localization and mapping based on fringe projection profilometry
Simultaneous Localization and Mapping (SLAM) plays an important role in outdoor and indoor applications ranging from autonomous driving to indoor robotics.
A Summary of the ComParE COVID-19 Challenges
The COVID-19 pandemic has caused massive humanitarian and economic damage.
Exploring Longitudinal Cough, Breath, and Voice Data for COVID-19 Progression Prediction via Sequential Deep Learning: Model Development and Validation
Recent work has shown the potential of using audio data (eg, cough, breathing, and voice) in the screening for COVID-19.
Benchmarking Uncertainty Quantification on Biosignal Classification Tasks under Dataset Shift
A biosignal is a signal that can be continuously measured from human bodies, such as respiratory sounds, heart activity (ECG), brain waves (EEG), etc, based on which, machine learning models have been developed with very promising performance for automatic disease detection and health status monitoring.
A Reinforcement Learning-based Adaptive Control Model for Future Street Planning, An Algorithm and A Case Study
An intelligent street can learn and improve its decision-making on the right-of-way (ROW) for road users, liberating more active pedestrian space while maintaining traffic safety and efficiency.
Dual camera snapshot hyperspectral imaging system via physics informed learning
Various methods using CNNs have been developed in recent years to reconstruct HSIs, but most of the supervised deep learning methods aimed to fit a brute-force mapping relationship between the captured compressed image and standard HSIs.
Segmentation-free Heart Pathology Detection Using Deep Learning
Cardiovascular (CV) diseases are the leading cause of death in the world, and auscultation is typically an essential part of a cardiovascular examination.
Sounds of COVID-19: exploring realistic performance of audio-based digital testing
In this paper, we explore the realistic performance of audio-based digital testing of COVID-19.
Fitbeat: COVID-19 Estimation based on Wristband Heart Rate
This study investigates the potential of deep learning methods to identify individuals with suspected COVID-19 infection using remotely collected heart-rate data.
Uncertainty-Aware COVID-19 Detection from Imbalanced Sound Data
To handle these issues, we propose an ensemble framework where multiple deep learning models for sound-based COVID-19 detection are developed from different but balanced subsets from original data.
The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates
The INTERSPEECH 2021 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the COVID-19 Cough and COVID-19 Speech Sub-Challenges, a binary classification on COVID-19 infection has to be made based on coughing sounds and speech; in the Escalation SubChallenge, a three-way assessment of the level of escalation in a dialogue is featured; and in the Primates Sub-Challenge, four species vs background need to be classified.
Compositional Prototype Network with Multi-view Comparision for Few-Shot Point Cloud Semantic Segmentation
Moreover, when we use our network to handle the long-tail problem in a fully supervised point cloud segmentation dataset, it can also effectively boost the performance of the few-shot classes.
LiDAR Data Enrichment Using Deep Learning Based on High-Resolution Image: An Approach to Achieve High-Performance LiDAR SLAM Using Low-cost LiDAR
Inspired by the complementariness of 3D LiDAR and camera, this paper proposes to make use of the high-resolution images from a camera to enrich the raw 3D point clouds from the low-cost 16 channels LiDAR based on a state-of-the-art deep learning algorithm.
Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data
This work opens the door to further investigation of how automatically analysed respiratory patterns could be used as pre-screening signals to aid COVID-19 diagnosis.
An Early Study on Intelligent Analysis of Speech under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety
In particular, by analysing speech recordings from these patients, we construct audio-only-based models to automatically categorise the health state of patients from four aspects, including the severity of illness, sleep quality, fatigue, and anxiety.
Learning-based real-time method to looking through scattering medium beyond the memory effect
Strong scattering medium brings great difficulties to optical imaging, which is also a problem in medical imaging and many other fields.
EmoBed: Strengthening Monomodal Emotion Recognition via Training with Crossmodal Emotion Embeddings
Motivated by this, we propose a novel crossmodal emotion embedding framework called EmoBed, which aims to leverage the knowledge from other auxiliary modalities to improve the performance of an emotion recognition system at hand.
High Sensitivity Snapshot Spectrometer Based on Deep Network Unmixing
In this paper, we present a convolution neural network based method to recover the light intensity distribution from the overlapped dispersive spectra instead of adding an extra light path to capture it directly for the first time.
Non-destructive three-dimensional measurement of hand vein based on self-supervised network
At present, supervised stereo methods based on deep neural network have achieved impressive results.
Snore-GANs: Improving Automatic Snore Sound Classification with Synthesized Data
One of the frontier issues that severely hamper the development of automatic snore sound classification (ASSC) associates to the lack of sufficient supervised training data.
Residual Pyramid Learning for Single-Shot Semantic Segmentation
In this paper, we put forward a method for single-shot segmentation in a feature residual pyramid network (RPNet), which learns the main and residuals of segmentation by decomposing the label at different levels of residual blocks.
SEWA DB: A Rich Database for Audio-Visual Emotion and Sentiment Research in the Wild
Natural human-computer interaction and audio-visual human behaviour sensing systems, which would achieve robust performance in-the-wild are more needed than ever as digital devices are increasingly becoming an indispensable part of our life.
Scaling Speech Enhancement in Unseen Environments with Noise Embeddings
We address the problem of speech enhancement generalisation to unseen environments by performing two manipulations.
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives
Over the past few years, adversarial training has become an extremely active research topic and has been successfully applied to various Artificial Intelligence (AI) domains.
Learning audio sequence representations for acoustic event classification
Extensive evaluation on a large-size acoustic event database is performed, and the empirical results demonstrate that the learnt audio sequence representation yields a significant performance improvement by a large margin compared with other state-of-the-art hand-crafted sequence features for AEC.
Learning to Refine Object Contours with a Top-Down Fully Convolutional Encoder-Decoder Network
We develop a novel deep contour detection algorithm with a top-down fully convolutional encoder-decoder network.
