Graph Neural Networks (GNNs) have become the state-of-the-art method for many applications on graph structured data. GNNs are a model for graph representation learning, which aims at learning to generate low dimensional node embeddings that encapsulate structural and feature-related information. GNNs are usually trained in an end-to-end fashion, leading to highly specialized node embeddings. While this approach achieves great results in the single-task setting, the generation of node embeddings that can be used to perform multiple tasks (with performance comparable to single-task models) is still an open problem. We propose the use of meta-learning to allow the training of a GNN model capable of producing multi-task node embeddings. In particular, we exploit the properties of optimization-based meta-learning to learn GNNs that can produce general node representations by learning parameters that can quickly (i.e. with a few steps of gradient descent) adapt to multiple tasks. Our experiments show that the embeddings produced by a model trained with our purposely designed meta-learning procedure can be used to perform multiple tasks with comparable or, surprisingly, even higher performance than both single-task and multi-task end-to-end models.
从场景图生成图像是显式场景生成和操作的一个很有前途的方向。然而，从场景图生成的图像缺乏质量，这部分是由于数据的高难度和多样性。我们提出了MIGS（Meta Image Generation from Scene Graphs），这是一种基于元学习的方法，用于从图中生成Few-Shot图像，使模型能够适应不同的场景，并通过对不同的任务集进行训练来提高图像质量。通过以任务驱动的方式对数据进行采样，我们使用元学习对基于场景属性分类的不同任务集进行训练生成器。我们的研究结果表明，使用这种元学习方法从场景图中生成图像，在图像质量和捕捉场景中的语义关系方面实现了最先进的性能。项目网址：https://migs2021.github.io/
