5 accepted papers
Enhancing the performance of semantic segmentation models with multi-spectral images (RGB-IR) is crucial, particularly for low-light and adverse environments. While multi-modal fusion techniques aim to learn cross-modality features for generating fused images or engage in knowledge distillation, the…
Deep neural networks have shown exemplary performance on semantic scene understanding tasks on source domains but due to the absence of style diversity during training enhancing performance on unseen target domains using only single source domain data remains a challenging task. Generation of simula…
Enhancing the generalization capacity for semantic segmentation of aerial perception systems for safety-critical applications is vital, especially for environments with low-light and adverse conditions. Multi-spectral fusion techniques aim to maintain the merits of electro-optical (EO) and infrared…
The scarcity of annotated infrared (IR) image datasets limits deep learning networks from achieving performances comparable to those achieved with RGB data. To address this, we introduce a novel semi-supervised RGB-to-IR Image-to-Image Translation model (SSL-RGB2IR) that generates synthetic IR data…
Deep learning models operating in the complex domain are used due to their rich representation capacity. However, most of these models are either restricted to the first quadrant of the complex plane or project the complex-valued data into the real domain, causing a loss of information. This paper p…