Traffic accident detection and anticipation is an obstinate road safety problem and
painstaking efforts have been devoted. With the rapid growth of video data, Vision-based …

In this paper, we implement a deep learning model for car accident detection using synthetic
videos while adapting the model, using domain adaptation (DA), to real videos from CCTV …

Convolutional neural networks raised the bar for machine learning and artificial intelligence
applications, mainly due to the abundance of data and computations. However, there is not …

Detecting objects with a small representation in images is a challenging task, especially
when the style of the images is very different from recent photos, which is the case for …

The biggest fear when deploying machine learning models to the real world is their ability to
handle the new data. This problem is significant especially in medicine, where models …

Image classification models have improved drastically due to neural networks. But as a
direct consequence of being trained on a specific dataset, neural networks tend to be biased …

Landmark detection is an essential step in the diagnosis of bone pathologies and pelvis
morphometry. Hence, we propose a Deep Learning based method for automatic landmark …

Object detection and Image classification have witnessed tremendous improvement in fixing
domain gaps between training and deployment data. However, Transfer Learning is still the …

Abstract The Unsupervised Domain Adaptation (UDA) methods aim to enhance feature
transferability possibly at the expense of feature discriminability. Recently, contrastive …

Abstract Domain generalization is a sub-field of transfer learning that aims at bridging the
gap between two different domains in the absence of any knowledge about the target …