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Found 25 papers, 3 papers with code
Advancing Explainable AI Toward Human-Like Intelligence: Forging the Path to Artificial Brain
The intersection of Artificial Intelligence (AI) and neuroscience in Explainable AI (XAI) is pivotal for enhancing transparency and interpretability in complex decision-making processes.
Reconstructing the Invisible: Video Frame Restoration through Siamese Masked Conditional Variational Autoencoder
In the domain of computer vision, the restoration of missing information in video frames is a critical challenge, particularly in applications such as autonomous driving and surveillance systems.
Triamese-ViT: A 3D-Aware Method for Robust Brain Age Estimation from MRIs
However, the untapped potential of Vision Transformers (ViTs), known for their accuracy and interpretability, persists in this domain due to limitations in their 3D versions.
Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions
As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Marine Debris Detection in Satellite Surveillance using Attention Mechanisms
Box detection assessment revealed that CBAM achieved the best outcome (F1 score of 77%) compared to coordinate attention (F1 score of 71%) and YOLOv7/bottleneck transformer (both F1 scores around 66%).
Semantic Segmentation under Adverse Conditions: A Weather and Nighttime-aware Synthetic Data-based Approach
Recent semantic segmentation models perform well under standard weather conditions and sufficient illumination but struggle with adverse weather conditions and nighttime.
Leveraging Synthetic Data to Learn Video Stabilization Under Adverse Conditions
In this paper, we propose a synthetic-aware adverse weather robust algorithm for video stabilization that does not require real data and can be trained only on synthetic data.
Review on Social Behavior Analysis of Laboratory Animals: From Methodologies to Applications
As the bridge between genetic and physiological aspects, animal behaviour analysis is one of the most significant topics in biology and ecological research.
Machine Learning-based Biological Ageing Estimation Technologies: A Survey
In recent years, there are various methods of estimating Biological Age (BA) have been developed.
Self-Supervised Leaf Segmentation under Complex Lighting Conditions
As an essential prerequisite task in image-based plant phenotyping, leaf segmentation has garnered increasing attention in recent years.
Private Facial Diagnosis as an Edge Service for Parkinson's DBS Treatment Valuation
Facial phenotyping has recently been successfully exploited for medical diagnosis as a novel way to diagnose a range of diseases, where facial biometrics has been revealed to have rich links to underlying genetic or medical causes.
Robust and integrative Bayesian neural networks for likelihood-free parameter inference
State-of-the-art neural network-based methods for learning summary statistics have delivered promising results for simulation-based likelihood-free parameter inference.
Deep Learning based Automated Forest Health Diagnosis from Aerial Images
Following the automated detection, we are able to automatically produce and calculate number of dead tree masks to label the dead trees in an image, as an indicator of forest health that could be linked to the causal analysis of environmental changes and the predictive likelihood of forest fire.
A Deep Learning based Wearable Healthcare IoT Device for AI-enabled Hearing Assistance Automation
With the recent booming of artificial intelligence (AI), particularly deep learning techniques, digital healthcare is one of the prevalent areas that could gain benefits from AI-enabled functionality.
3D Printed Brain-Controlled Robot-Arm Prosthetic via Embedded Deep Learning from sEMG Sensors
In this paper, we present our work on developing robot arm prosthetic via deep learning.
In-Vehicle Object Detection in the Wild for Driverless Vehicles
In-vehicle human object identification plays an important role in vision-based automated vehicle driving systems while objects such as pedestrians and vehicles on roads or streets are the primary targets to protect from driverless vehicles.
Automated Blood Cell Detection and Counting via Deep Learning for Microfluidic Point-of-Care Medical Devices
The approach we used in this study was based on Faster Region-based Convolutional Neural Networks (Faster RCNNs), and a transfer learning process was applied to apply this technique to the microscopic detection of blood cells.
Computer-Aided Automated Detection of Gene-Controlled Social Actions of Drosophila
Gene expression of social actions in Drosophilae has been attracting wide interest from biologists, medical scientists and psychologists.
Atypical Facial Landmark Localisation with Stacked Hourglass Networks: A Study on 3D Facial Modelling for Medical Diagnosis
While facial biometrics has been widely used for identification purpose, it has recently been researched as medical biometrics for a range of diseases.
Biometric Blockchain: A Better Solution for the Security and Trust of Food Logistics
The advantage of using BBC in the food logistics is clear: it can not only identify if the data or labels are authentic, but also clearly record who is responsible for the secured data or labels.
Shallow Unorganized Neural Networks using Smart Neuron Model for Visual Perception
In this paper, we propose a new computational model, namely shallow unorganized neural networks (SUNNs), in contrast to ANNs/DNNs.
3DPalsyNet: A Facial Palsy Grading and Motion Recognition Framework using Fully 3D Convolutional Neural Networks
The capability to perform facial analysis from video sequences has significant potential to positively impact in many areas of life.
Distant Pedestrian Detection in the Wild using Single Shot Detector with Deep Convolutional Generative Adversarial Networks
In our work, GAN has been trained intensively on low resolution images, in order to neutralize the challenges of the pedestrian detection in the wild, and considered humans, and few other classes for detection in smart cities.
Social Behavioral Phenotyping of Drosophila with a2D-3D Hybrid CNN Framework
Behavioural phenotyping of Drosophila is an important means in biological and medical research to identify genetic, pathologic or psychologic impact on animal behaviour.
Deep Learning based Pedestrian Detection at Distance in Smart Cities
Generative adversarial networks (GANs) have been promising for many computer vision problems due to their powerful capabilities to enhance the data for training and test.
