9 accepted papers
Ensuring auditability and verifiability in FL is both challenging and essential to guarantee that local data remains untampered and client updates are trustworthy. Recent FL frameworks assess client contributions through a trusted central server using various client selection and aggregation techniq…
"Unsupervised domain adaptation (UDA) is a critical challenge in machine learning, aiming to transfer knowledge from a labeled source domain to an unlabeled target domain. In this work, we aim to improve target set accuracy in any existing UDA method by introducing an approach that utilizes pseudo-c…
Federated Learning (FL) facilitates clients to collaborate on training a shared machine learning model without exposing individual private data. Nonetheless FL remains susceptible to utility and privacy attacks notably evasion data poisoning and model inversion attacks compromising the system's effi…
Presents corrections to the article “Improving Multi-Agent Trajectory Prediction Using Traffic States on Interactive Driving Scenarios”.
Predicting trajectories of multiple agents in interactive driving scenarios such as intersections, and roundabouts are challenging due to the high density of agents, varying speeds, and environmental obstacles. Existing approaches use relative distance and semantic maps of intersections to improve t
Domain Generalization (DG) techniques have emerged as a popular approach to address the challenges of domain shift in Deep Learning (DL), with the goal of generalizing well to the target domain unseen during the training. In recent years, numerous methods have been proposed to address the DG setting…
Representation and modelling of movements play a significant role in recognising actions in unconstrained videos. However, explicit segmentation and labelling of movements are non-trivial because of the variability associated with actors, camera viewpoints, duration etc. Therefore, we propose to tra…
In this paper, an approach for classification of medical images using edge-based features is proposed. We demonstrate that the edge information extracted from an image by dividing the image into patches and each patch into concentric circular regions provide discriminative information useful for cla…
Feature extraction is one of the most important phases of medical image classification which requires extensive domain knowledge. Convolutional Neural Networks (CNN) have been successfully used for feature extraction in images from different domains involving a lot of classes. In this paper, CNNs ar…