论文标题
深入学习物质识别：最新进展和开放挑战
Deep Learning for Material recognition: most recent advances and open challenges
论文作者
论文摘要
从彩色图像中识别材料仍然是一个具有挑战性的问题。虽然深层神经网络在对象识别方面提供了很好的结果，并且在过去十年中一直是大量论文的主题，但它们对材料图像的适应仍然需要一些作品才能达到同等准确性。然而，最近的研究在深入学习的材料识别方面取得了很好的成果，我们在本文中建议通过关注三个方面来审查其中的大多数：材料图像数据集，上下文的影响和临时描述符以供材料外观。每个方面都是通过系统的方式引入的，并引用了代表性作品的结果。我们还在该领域展示了自己的研究，并指出了未来作品的一些开放挑战。
Recognizing material from color images is still a challenging problem today. While deep neural networks provide very good results on object recognition and has been the topic of a huge amount of papers in the last decade, their adaptation to material images still requires some works to reach equivalent accuracies. Nevertheless, recent studies achieve very good results in material recognition with deep learning and we propose, in this paper, to review most of them by focusing on three aspects: material image datasets, influence of the context and ad hoc descriptors for material appearance. Every aspect is introduced by a systematic manner and results from representative works are cited. We also present our own studies in this area and point out some open challenges for future works.
