Complex inverse synthetic aperture radar (ISAR) object recognition is a critical and challenging problem in computer vision tasks. An efficient complex object recognition method for ISAR images is proposed based on adaptive deep relation learning. (i) An adaptive multimodal mechanism is proposed to greatly improve the multimodal sampling and transformation capabilities of convolutional neural networks and increase the resolutions of the feature maps. In particular, an adaptive ranking and selection strategy for related regions are proposed. (ii) Deep graphical learning is proposed to jointly estimate large numbers of heterogeneous attributes and leverage the relations among features and attributes. Extensive experiments performed on real-world datasets show that the proposed method outperforms other state-of-the-art methods.

中文翻译：

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使用自适应深度关系学习的高效复杂ISAR目标识别

复杂的逆合成孔径雷达（ISAR）目标识别是计算机视觉任务中的关键和挑战性问题。提出了一种基于自适应深度关系学习的有效的ISAR图像复杂目标识别方法。（i）提出了一种自适应多峰机制，以大大提高卷积神经网络的多峰采样和转换能力，并提高特征图的分辨率。特别地，提出了针对相关区域的自适应排名和选择策略。（ii）提出了深度图形学习，以联合估计大量的异构属性并利用特征和属性之间的关系。在现实世界的数据集上进行的大量实验表明，该方法优于其他最新方法。