通过任务分解学习抽象视觉推理：以瑞文渐进矩阵（Raven Progressive Matrices）为例
Learning Abstract Visual Reasoning via Task Decomposition: A Case Study in Raven Progressive Matrices
AI总结：
本研究针对RPM抽象视觉推理难题，提出基于transformer的深度学习架构，通过任务分解预测物体属性与排列并并置结果选答，结合多种输入解析与掩码训练策略，性能超SOTA且可抗基准偏差。
摘要：
学习执行抽象推理通常需要将目标任务分解为中间子目标，这些子目标并非预先指定，而是需要学习者自主设计。在瑞文渐进矩阵（Raven Progressive Matrices, RPM）任务中，需要根据给定的上下文从可选答案中选出正确选项，其中上下文和答案均为包含多个处于不同空间排列的物体的复合图像。由于这一高层目标是唯一可用的指导，学习解决RPM问题颇具挑战性。本研究提出了一种基于transformer架构的深度学习模型，该模型并非直接做出上述选择，而是聚焦于预测单个物体的视觉属性及其排列方式的子目标。随后将通过这种方式得到的多维度预测结果直接并置，以选择答案。我们研究了模型将视觉输入解析为标记（token）的几种方式，以及自监督训练中对部分输入进行掩码处理的几种机制。在实验评估中，该模型不仅优于当前最优（state-of-the-art）方法，还为推理过程提供了有价值的见解和部分解释。该方法的设计还使其能够抵御部分RPM基准测试中已知存在的偏差。
英文摘要：
Learning to perform abstract reasoning often requires decomposing the task in question into intermediate subgoals that are not specified upfront, but need to be autonomously devised by the learner. In Raven Progressive Matrices (RPM), the task is to choose one of the available answers given a context, where both the context and answers are composite images featuring multiple objects in various spatial arrangements. As this high-level goal is the only guidance available, learning to solve RPMs is challenging. In this study, we propose a deep learning architecture based on the transformer blueprint which, rather than directly making the above choice, addresses the subgoal of predicting the visual properties of individual objects and their arrangements. The multidimensional predictions obtained in this way are then directly juxtaposed to choose the answer. We consider a few ways in which the model parses the visual input into tokens and several regimes of masking parts of the input in self-supervised training. In experimental assessment, the models not only outperform state-of-the-art methods but also provide interesting insights and partial explanations about the inference. The design of the method also makes it immune to biases that are known to be present in some RPM benchmarks.
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。
