The 3rd Anti-UAV Workshop & Challenge aims to encourage research in developing novel
and accurate methods for multi-scale object tracking. The Anti-UAV dataset used for the Anti-…

To interpret deep neural networks, one main approach is to dissect the visual input and find
the prototypical parts responsible for the classification. However, existing methods often …

Facial recognition systems have achieved remarkable success by leveraging deep neural
networks, advanced loss functions, and large-scale datasets. However, their performance …

Aims Automated interpretation of electrocardiograms (ECGs) using deep neural networks (DNNs)
has gained much attention recently. While the initial results have been encouraging, …

Background Clinical data used to train deep learning models are often not clean data. They
can contain imperfections in both the imaging data and the corresponding segmentations. …

In this paper, our aim is to provide human understandable intuitive factual and counterfactual
explanations for the decisions of neural networks. Humans tend to reinforce their decisions …

In this paper, we aim to explain the decisions of neural networks by utilizing multimodal
information. That is counter-intuitive attributes and counter visual examples which appear when …

In Pakistan it is a right-hand drive, while in the Netherlands it is a left-hand drive. More
importantly, the traffic and weather conditions are significantly different. In Pakistan, a driver …

This paper delves into the results of two resource-constrained deep learning challenges, part
of the workshop on Resource-Efficient Deep Learning for Computer Vision (RCV) at ICCV …

This paper proposes a new architecture Deep Convolutional and Recurrent writer (DCRW)
for image generation by adapting the deep Recurrent attentive writer (DRAW) architecture …