Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes (2101.06085v2)

Abstract: Semantic segmentation is a key technology for autonomous vehicles to understand the surrounding scenes. The appealing performances of contemporary models usually come at the expense of heavy computations and lengthy inference time, which is intolerable for self-driving. Using light-weight architectures (encoder-decoder or two-pathway) or reasoning on low-resolution images, recent methods realize very fast scene parsing, even running at more than 100 FPS on a single 1080Ti GPU. However, there is still a significant gap in performance between these real-time methods and the models based on dilation backbones. To tackle this problem, we proposed a family of efficient backbones specially designed for real-time semantic segmentation. The proposed deep dual-resolution networks (DDRNets) are composed of two deep branches between which multiple bilateral fusions are performed. Additionally, we design a new contextual information extractor named Deep Aggregation Pyramid Pooling Module (DAPPM) to enlarge effective receptive fields and fuse multi-scale context based on low-resolution feature maps. Our method achieves a new state-of-the-art trade-off between accuracy and speed on both Cityscapes and CamVid dataset. In particular, on a single 2080Ti GPU, DDRNet-23-slim yields 77.4% mIoU at 102 FPS on Cityscapes test set and 74.7% mIoU at 230 FPS on CamVid test set. With widely used test augmentation, our method is superior to most state-of-the-art models and requires much less computation. Codes and trained models are available online.

• The paper introduces a dual-resolution network architecture that balances high-detail spatial information with computational efficiency.
• It achieves real-time segmentation performance on road scenes, demonstrating competitive accuracy on benchmark datasets.
• The method offers promising applications in autonomous driving and advanced driver-assistance systems.

An Examination of "Bare Advanced Demo of IEEEtran.cls for IEEE Computer Society Journals"

This document, authored by Michael Shell, John Doe, and Jane Doe, offers insights into the utilization of the IEEEtran.cls version 1.8b for creating LaTeX documents intended for IEEE Computer Society journals. Although the document is a template, it provides valuable guidelines and serves as a practical example for authors navigating the complexities of IEEE journal formatting.

Overview

The primary endeavor of the document is to create a "starter file" that guides authors through the creation and structuring of articles in accordance with IEEE Computer Society's formatting requirements. It is structured to include sections commonly found in academic papers, such as the abstract, keywords, introduction, and conclusions.

Technical Aspects

The template delivers several significant LaTeX functionalities that are crucial for authors:

• Title and Author Declarations: The template encapsulates methods for title presentation and proper attribution of authorship using the \texttt{IEEEcompsocitemizethanks} environment.
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• Appendices and Acknowledgments: The template also delineates the structure for adding appendices and acknowledgments, which can be pivotal for work requiring supplementary material or acknowledgments of contributions.

Practical Implications

The document is highly beneficial for researchers unfamiliar with IEEE standards, as it simplifies the adoption of these criteria through a practical example. By integrating this template into their writing process, researchers can significantly reduce errors related to formatting. The automated features of the IEEEtran class, such as counters and bibliography management, facilitate effective and efficient manuscript preparation, ultimately allowing researchers to focus more on the substance of their work rather than its format.

Future Directions and Speculations

Looking ahead, enhancements in LaTeX templates like IEEEtran.cls could include greater automation and more intuitive features to streamline the writing process further. As the Open Source community continues to evolve, we might see integration with GUI tools that make these powerful template styles more accessible to all researchers, beyond those already versed in LaTeX.

In conclusion, this template is a critical resource for academic authors aiming to comply with IEEE publication standards. It demonstrates essential formatting requirements while providing an operational foundation for further document customization. This approach promises to enhance document preparation and submission efficiency for both novice and experienced researchers in the domain.