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Found 22 papers, 6 papers with code
Estimating the Robustness Radius for Randomized Smoothing with 100$\times$ Sample Efficiency
Randomized smoothing (RS) has successfully been used to improve the robustness of predictions for deep neural networks (DNNs) by adding random noise to create multiple variations of an input, followed by deciding the consensus.
Uncertainty-guided Contrastive Learning for Single Source Domain Generalisation
In the context of single domain generalisation, the objective is for models that have been exclusively trained on data from a single domain to demonstrate strong performance when confronted with various unfamiliar domains.
COVID-19 Computer-aided Diagnosis through AI-assisted CT Imaging Analysis: Deploying a Medical AI System
Computer-aided diagnosis (CAD) systems stand out as potent aids for physicians in identifying the novel Coronavirus Disease 2019 (COVID-19) through medical imaging modalities.
Domain adaptation, Explainability & Fairness in AI for Medical Image Analysis: Diagnosis of COVID-19 based on 3-D Chest CT-scans
The paper presents the DEF-AI-MIA COV19D Competition, which is organized in the framework of the 'Domain adaptation, Explainability, Fairness in AI for Medical Image Analysis (DEF-AI-MIA)' Workshop of the 2024 Computer Vision and Pattern Recognition (CVPR) Conference.
Self-Supervised Learning for Visual Relationship Detection through Masked Bounding Box Reconstruction
We present a novel self-supervised approach for representation learning, particularly for the task of Visual Relationship Detection (VRD).
A Deep Neural Architecture for Harmonizing 3-D Input Data Analysis and Decision Making in Medical Imaging
Harmonizing the analysis of data, especially of 3-D image volumes, consisting of different number of slices and annotated per volume, is a significant problem in training and using deep neural networks in various applications, including medical imaging.
AI-MIA: COVID-19 Detection & Severity Analysis through Medical Imaging
This paper presents the baseline approach for the organized 2nd Covid-19 Competition, occurring in the framework of the AIMIA Workshop in the European Conference on Computer Vision (ECCV 2022).
DeepMoCap: Deep Optical Motion Capture Using Multiple Depth Sensors and Retro-Reflectors
DeepMoCap explores motion capture by automatically localizing and labeling reflectors on depth images and, subsequently, on 3D space.
HUMAN4D: A Human-Centric Multimodal Dataset for Motions and Immersive Media
HUMAN4D is introduced to the computer vision and graphics research communities to enable joint research on spatio-temporally aligned pose, volumetric, mRGBD and audio data cues.
MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis
In this paper we present the COV19-CT-DB database which is annotated for COVID-19, consisting of about 5, 000 3-D CT scans, We have split the database in training, validation and test datasets.
AI-enabled Efficient and Safe Food Supply Chain
This paper provides a review of an emerging field in the food processing sector, referring to efficient and safe food supply chains, from farm to fork, as enabled by Artificial Intelligence (AI).
An autoencoder wavelet based deep neural network with attention mechanism for multistep prediction of plant growth
Finally, a recurrent neural network including LSTM and an attention mechanism is proposed for modelling long-term dependencies in the time series data.
Introducing Routing Uncertainty in Capsule Networks
Rather than performing inefficient local iterative routing between adjacent capsule layers, we propose an alternative global view based on representing the inherent uncertainty in part-object assignment.
A compact sequence encoding scheme for online human activity recognition in HRI applications
Human activity recognition and analysis has always been one of the most active areas of pattern recognition and machine intelligence, with applications in various fields, including but not limited to exertion games, surveillance, sports analytics and healthcare.
Multi-Source Deep Domain Adaptation for Quality Control in Retail Food Packaging
The presence and accuracy of such information is critical to ensure a detailed understanding of the product and to reduce the potential for health risks.
A Unified Deep Learning Approach for Prediction of Parkinson's Disease
The paper presents a novel approach, based on deep learning, for diagnosis of Parkinson's disease through medical imaging.
Using Deep Learning to Predict Plant Growth and Yield in Greenhouse Environments
Effective plant growth and yield prediction is an essential task for greenhouse growers and for agriculture in general.
Capsule Routing via Variational Bayes
Capsule networks are a recently proposed type of neural network shown to outperform alternatives in challenging shape recognition tasks.
Ranked #3 on
Image Classification
on smallNORB
A Scalable Test Suite for Continuous Dynamic Multiobjective Optimisation
It is demonstrated with empirical studies that the proposed test suite is more challenging to the dynamic multiobjective optimisation algorithms found in the literature.
Predicting Parkinson's Disease using Latent Information extracted from Deep Neural Networks
This paper presents a new method for medical diagnosis of neurodegenerative diseases, such as Parkinson's, by extracting and using latent information from trained Deep convolutional, or convolutional-recurrent Neural Networks (DNNs).
Deep Bayesian Self-Training
Supervised Deep Learning has been highly successful in recent years, achieving state-of-the-art results in most tasks.
Towards a Deep Unified Framework for Nuclear Reactor Perturbation Analysis
512 dimensional representations were extracted from the 3D-CNN and LSTM architectures, and used as input to a fused multi-sigmoid classification layer to recognise the perturbation type.
