Computer Sciences & Mathematics Forum, Volume 9, Issue 1

Three-dimensional semantic segmentation is crucial for comprehending transmission line structure and environment. This understanding forms the basis for a variety of applications, such as automatic risk assessment of line tripping caused by wildfires...

Offline reinforcement learning leverages pre-collected datasets of transitions to train policies. It can serve as an effective initialization for online algorithms, enhancing sample efficiency and speeding up convergence. However, when such datasets...

Autonomously driving vehicles in car factories and parking spaces can represent a competitive advantage in the logistics industry. However, the real-world application is challenging in many ways. First of all, there are no publicly available datasets...

Vision-language models (VLMs) have demonstrated increasing potency in solving complex vision-language tasks in the recent past. Visual question answering (VQA) is one of the primary downstream tasks for assessing the capability of VLMs, as it helps i...

Standard training via empirical risk minimization may result in making predictions that overly rely on spurious correlations. This can degrade the generalization to out-of-distribution settings where these correlations no longer hold. Invariant learn...

In submitting conference proceedings to the Computer Sciences & Mathematics Forum, the volume editors of the proceedings certify to the publisher that all papers published in this volume have been subjected to peer review administered by the volu...