Complex visual scenes that are composed of multiple objects, each with attributes, such as object name, location, pose, color, etc., are challenging to describe in order to train neural networks. Usually,deep learning networks are trained supervised by categorical scene descriptions. The common categorical description of a scene contains the names of individual objects but lacks information about other attributes. Here, we use distributed representations of object attributes and vector operations in a vector symbolic architecture to create a full compositional description of a scene in a high-dimensional vector. To control the scene composition, we use artificial images composed of multiple, translated and colored MNIST digits. In contrast to learning category labels, here we train deep neural networks to output the full compositional vector description of an input image. The output of the deep network can then be interpreted by a VSA resonator network, to extract object identity or other properties of indiviual objects. We evaluate the performance and generalization properties of the system on randomly generated scenes. Specifically, we show that the network is able to learn the task and generalize to unseen seen digit shapes and scene configurations. Further, the generalisation ability of the trained model is limited. For example, with a gap in the training data, like an object not shown in a particular image location during training, the learning does not automatically fill this gap.

翻译：复杂视觉场景由多个对象组成，每个对象具有名称、位置、姿态、颜色等属性，此类场景的描述对训练神经网络构成挑战。通常，深度神经网络通过类别化场景描述进行监督学习。常见的场景类别描述包含单个对象的名称，但缺乏其他属性的信息。本文采用对象属性的分布式表示与向量符号架构中的向量运算，在高维向量中构建场景的完整组合描述。为控制场景组合，我们使用由多个经平移和着色处理的MNIST数字构成的人工图像。与学习类别标签不同，本文训练深度神经网络输出输入图像的完整组合向量描述。随后通过VSA谐振网络解析深度网络输出，提取对象身份或单个对象的其他属性。我们通过随机生成场景评估系统的性能与泛化特性。研究表明：网络能够学习任务并泛化至未见过的数字形状与场景配置；但训练模型的泛化能力存在局限——例如，当训练数据存在缺失（如某对象未在特定图像位置出现时），学习过程无法自动填补这一空白。