论文标题
从视觉数据中深入学习场景识别：调查
Deep learning for scene recognition from visual data: a survey
论文作者
论文摘要
在过去的几年中，深度学习技术的使用爆炸了，导致对人工智能领域的直接贡献。这项工作的目的是通过从视觉数据中深入学习模型对场景识别的最先进的审查。场景识别仍然是计算机视觉中的新兴领域，从单个图像和动态图像的角度来看，该领域已被解决。我们首先概述了可用的图像和视频场景识别的数据集。后来，我们描述了该领域研究论文引入的合奏技术。最后，我们对我们的发现发表了一些评论，并讨论了我们认为在研究领域和未来研究方面的挑战。本文旨在成为现场识别任务的模型选择的未来指南。
The use of deep learning techniques has exploded during the last few years, resulting in a direct contribution to the field of artificial intelligence. This work aims to be a review of the state-of-the-art in scene recognition with deep learning models from visual data. Scene recognition is still an emerging field in computer vision, which has been addressed from a single image and dynamic image perspective. We first give an overview of available datasets for image and video scene recognition. Later, we describe ensemble techniques introduced by research papers in the field. Finally, we give some remarks on our findings and discuss what we consider challenges in the field and future lines of research. This paper aims to be a future guide for model selection for the task of scene recognition.
