6 accepted papers
The complexity of EEG data presents significant challenges for accurate diagnosis in neurological conditions such as Alzheimer’s disease. In this paper, we introduce Explainable Orthogonal Attention Networks, a novel approach for EEG-based analysis that decouples spatial and temporal features to mor…
Stereo image super-resolution (SR) aims to enhance image resolution by leveraging the complementary information from stereo image pairs. While convolutional neural network (CNN)-based methods have traditionally dominated this field, they struggle with capturing long-range dependencies. Transformer-b…
Explainable decision-making is critical for building trust in autonomous vehicles. We investigate the use of a pre-trained large language model (LLM) to derive comprehensible driving decisions from multi-modal time-series data captured by a monocular camera on an autonomous vehicle. Leveraging a gra…
One of the main challenges in designing deep learning networks for autonomous driving is the lack of labeled data. Recent trends that address this problem involve the use of unlabeled data. In this paper, we propose a unified semi-supervised and federated learning (FL) approach that is designed to o…
To improve compression efficiency, pixels are traditionally represented using a luma and two chroma values. Such a representation aims at separating light from color information. Two methods are usually considered for computing luma values: Non-Constant Luminance (NCL) and Constant Luminance (CL). C…
Color pixel encoding optimizes the conversion of linear physical values of light into integer values. The efficiency of such encoding methods depends on a trade-off between the bit-depth used and the visible distortion introduced by quantization. This efficiency for different color pixel encoding ap…