[论文解读] Segment Anything in Non-Euclidean Domains: Challenges and Opportunities

Yongcheng Jing, Xinchao Wang+1 more|arXiv (Cornell University)|Apr 23, 2023

Advanced Graph Neural Networks被引用 9

一句话总结

该论文提出 Segment Non-Euclidean Anything (SNA)，一个受 foundation-model 启发的图数据框架，并提出 meta-slimmable 图处理加上一个基于提示的分析框架，初步实验表明当前的朴素方法在通用图任务上不足以胜任。

ABSTRACT

The recent work known as Segment Anything (SA) has made significant strides in pushing the boundaries of semantic segmentation into the era of foundation models. The impact of SA has sparked extremely active discussions and ushered in an encouraging new wave of developing foundation models for the diverse tasks in the Euclidean domain, such as object detection and image inpainting. Despite the promising advances led by SA, the concept has yet to be extended to the non-Euclidean graph domain. In this paper, we explore a novel Segment Non-Euclidean Anything (SNA) paradigm that strives to develop foundation models that can handle the diverse range of graph data within the non-Euclidean domain, seeking to expand the scope of SA and lay the groundwork for future research in this direction. To achieve this goal, we begin by discussing the recent achievements in foundation models associated with SA. We then shed light on the unique challenges that arise when applying the SA concept to graph analysis, which involves understanding the differences between the Euclidean and non-Euclidean domains from both the data and task perspectives. Motivated by these observations, we present several preliminary solutions to tackle the challenges of SNA and detail their corresponding limitations, along with several potential directions to pave the way for future SNA research. Experiments on five Open Graph Benchmark (OGB) datasets across various tasks, including graph property classification and regression, as well as multi-label prediction, demonstrate that the performance of the naive SNA solutions has considerable room for improvement, pointing towards a promising avenue for future exploration of Graph General Intelligence.

研究动机与目标

• 将 foundation 模型和 Segment Anything 范式扩展到非欧几里得图数据的动机。
• 为跨多样化图类型和任务的通用图分析定义 Segment Non-Euclidean Anything (SNA) 任务。
• 识别图 foundation 模型在数据维度和任务异质性方面的关键挑战。
• 提出初步解决方案（meta-slimmable 图处理和提示式框架）来应对这些挑战。
• 提供初步实验以说明朴素方法的局限性并为未来研究指明方向。

提出的方法

• Meta-Slimmable Graph Processing，通过学习根据下游任务选择最优神经元来应对输入输出维度的变化。
• 用 Slimmable GCN 替换第一层和最后一层 GCN，以实现对不同特征维度的图进行处理。
• Prompt-based Foundation Framework（prompt-based SNA），通过一个带有输入输出对的示例图来引导下游在新图上的任务。
• 借鉴 Segment Anything 的示例式提示，用于支持包括传导/归纳节点预测、边预测以及图分类/回归等多样化图任务的示例驱动提示。
• 分析由异构图结构、特征维度和任务多样性引发的数据端和任务端挑战。

实验结果

研究问题

• RQ1 在将 Segment Anything 范式引入非欧几里得图数据时，面临的基本挑战是什么？
• RQ2 是否能够开发出类似 foundation 模型的 SNA，以处理跨域的多样化图结构、特征和任务？
• RQ3 在非欧几里得域中，现有的朴素图方法是否足以实现通用图分析，还是需要新的方法学？
• RQ4 哪些初步解决方案能够应对 SNA 中的维度异质性和任务多样性，以及它们的局限性？

主要发现

• 在五个 OGB 数据集上的初步实验表明，朴素的 SNA 方法在包括图分类、回归和多标签预测在内的多种任务上仍有较大提升空间。
• 一个朴素的 meta-slimmable GCN 能适应不同的输入特征维度，但在所有任务上尚未达到有竞争力的表现。
• 基于提示的 SNA 框架通过示例输入输出为适应下游图提供了一个概念性方案，但仍处于早期阶段，证据有限。
• 在所测试的基准上，使用朴素方法进行预训练和下游迁移与作为通用图模型预期之间存在性能差距。
• 结果凸显了在非欧几里得领域向图通用智能迈进需要更先进的方法。