从纠缠场景表征中发现对象及其关系
Discovering objects and their relations from entangled scene representations
摘要：
我们的世界可以简洁而紧凑地描述为物体和关系的结构化场景。例如，一个典型的房间包含桌子、椅子和书籍等突出的对象，这些对象通常通过其潜在的原因和语义相互关联。这就产生了相关的特征，如位置、功能和形状。人类利用对象及其关系的知识来学习广泛的任务，更普遍的是学习观察数据的结构。在这项工作中，我们介绍了关系网络（RNs）-一个通用的神经网络架构的对象关系推理。我们表明，RNs能够从场景描述数据中学习对象关系。此外，我们表明，RNs可以作为一个瓶颈，诱导对象的因式分解纠缠场景描述输入，并从分布式的深度表示的场景图像提供的变分自编码器。该模型还可以与微分记忆机制结合使用，用于一次性学习任务中的内隐关系发现。我们的研究结果表明，关系网络是一个潜在的强大的架构，解决各种问题，需要对象关系推理。
英文摘要：
Our world can be succinctly and compactly described as structured scenes of objects and relations. A typical room, for example, contains salient objects such as tables, chairs and books, and these objects typically relate to each other by their underlying causes and semantics. This gives rise to correlated features, such as position, function and shape. Humans exploit knowledge of objects and their relations for learning a wide spectrum of tasks, and more generally when learning the structure underlying observed data. In this work, we introduce relation networks (RNs) - a general purpose neural network architecture for object-relation reasoning. We show that RNs are capable of learning object relations from scene description data. Furthermore, we show that RNs can act as a bottleneck that induces the factorization of objects from entangled scene description inputs, and from distributed deep representations of scene images provided by a variational autoencoder. The model can also be used in conjunction with differentiable memory mechanisms for implicit relation discovery in one-shot learning tasks. Our results suggest that relation networks are a potentially powerful architecture for solving a variety of problems that require object relation reasoning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
