TADM: Temporally-Aware Diffusion Model for Neurodegenerative Progression on Brain MRI
Generating realistic images to accurately predict changes in the structure of brain MRI can be a crucial tool for clinicians. Such applications can help assess patients’ outcomes and analyze how diseases progress at the individual level. However, existing methods developed for this task present some l...
Synchronization is All You Need: Exocentric-to-Egocentric Transfer for Temporal Action Segmentation with Unlabeled Synchronized Video Pairs
We consider the problem of transferring a temporal action segmentation system initially designed for exocentric (fixed) cameras to an egocentric scenario, where wearable cameras capture video data. The conventional supervised approach requires the collection and labeling of a new set of egocentric videos...
Unsupervised anomaly detection for pome fruit quality inspection using X-ray radiography
A novel fully convolutional autoencoder (convAE) was introduced to analyze X-ray radiography images of ‘Braeburn’ apples and ‘Conference’ pears with and without disorders for online sorting purposes. The model was solely trained on either apple or pear samples without disorders and outperformed a tradi...
On the cloud detection from backscattered images generated from a lidar-based ceilometer: Current state and opportunities
Accurate weather monitoring depends significantly on cloud detection, a crucial process achievable through remote sensing tools such as satellite imagery and radar or through the analysis of data obtained from ceilometers. A ceilometer is a lidar-based device allowing to analyse the atmosphere and detect...
Federated Learning in a Semi-Supervised Environment for Earth Observation Data
We propose FedRec, a federated learning workflow taking advantage of unlabelled data in a semi-supervised environment to assist in the training of a supervised aggregated model. In our proposed method, an encoder architecture extracting features from unlabelled data is aggregated with the feature extractor...
Evaluating Language Model Vulnerability to Poisoning Attacks in Low-Resource Settings
Deep learning-based language models have achieved state-of-the-art results in a number of applications including sentiment analysis, topic labelling, intent classification and others. Obtaining text representations or embeddings using these models presents the possibility of encoding personally identifiable...