Traffic Signs Detection and Localisation

Cieľom tejto bakalárskej práce je navrhnúť jednoduchý systém pre detekciu a lokalizáciu dopravného značenia v obraze s využitím už existujúcich riešení. Detekcia značiek prebieha na základe ich tvarov a lokalizácia detekovaných objektov prostredníctvom dat z LIDARu. Vytvorené riešenie pozostáva z dvoch komponent: z detektora a lokalizátora pričom každá dokáže pracovať samostatne.This thesis aims to design the traffic signs detection and localization system using RGB image and 3D LiDAR data leveraging the the existing solutions. Traffic sign detection is based on the shape analysis. Then, the LIDAR data are used for the localization of previously detected signs. The created solution consists of two main components: the detector and locator, each able to operate independently.

Information Theory-Guided Heuristic Progressive Multi-View Coding

Multi-view representation learning aims to capture comprehensive information from multiple views of a shared context. Recent works intuitively apply contrastive learning to different views in a pairwise manner, which is still scalable: view-specific noise is not filtered in learning view-shared representations; the fake negative pairs, where the negative terms are actually within the same class as the positive, and the real negative pairs are coequally treated; evenly measuring the similarities between terms might interfere with optimization. Importantly, few works study the theoretical framework of generalized self-supervised multi-view learning, especially for more than two views. To this end, we rethink the existing multi-view learning paradigm from the perspective of information theory and then propose a novel information theoretical framework for generalized multi-view learning. Guided by it, we build a multi-view coding method with a three-tier progressive architecture, namely Information theory-guided hierarchical Progressive Multi-view Coding (IPMC). In the distribution-tier, IPMC aligns the distribution between views to reduce view-specific noise. In the set-tier, IPMC constructs self-adjusted contrasting pools, which are adaptively modified by a view filter. Lastly, in the instance-tier, we adopt a designed unified loss to learn representations and reduce the gradient interference. Theoretically and empirically, we demonstrate the superiority of IPMC over state-of-the-art methods.Comment: This paper is accepted by the jourcal of Neural Networks (Elsevier) by 2023. A revised manuscript of arXiv:2109.0234