Understanding sequential information is a fundamental task for artificial intelligence. Current neural networks attempt to learn spatial and temporal information as a whole, limiting their abilities to represent large-scale spatial representations over long-range sequences. Here, we introduce a new modelling strategy—‘semi-coupled structure’ (SCS)—which consists of deep neural networks that decouple the complex spatial and temporal concepts during learning. SCS can learn to implicitly separate input information into independent parts and process these parts separately. Experiments demonstrate that SCS can successfully sequentially annotate the outline of an object in images and perform video action recognition. As an example of sequence-to-sequence problems, SCS can predict future meteorological radar echo images based on observed images. Taken together, our results demonstrate that SCS has the capacity to improve the performance of long short-term memory (LSTM)-like models on large-scale sequential tasks.

了解顺序信息是人工智能的基本任务。当前的神经网络试图整体上学习空间和时间信息，从而限制了它们在远程序列上代表大规模空间表示的能力。在这里，我们介绍一种新的建模策略-“半耦合结构”（SCS），该策略由深度神经网络组成，这些神经网络在学习过程中将复杂的时空概念解耦。SCS可以学习将输入信息隐式地分为独立的部分，并分别处理这些部分。实验表明，SCS可以成功地顺序注释图像中对象的轮廓并执行视频动作识别。作为逐个序列问题的一个示例，SCS可以根据观察到的图像预测未来的气象雷达回波图像。