A-I-RAVEN与I-RAVEN-Mesh：两个面向抽象视觉推理的新基准
A-I-RAVEN and I-RAVEN-Mesh: Two New Benchmarks for Abstract Visual Reasoning
AI总结：
针对抽象视觉推理领域的模型泛化与知识迁移评估需求，提出A-I-RAVEN与I-RAVEN-Mesh两个新基准，经13个主流模型测试揭示了现有方法的相关缺陷。
摘要：
我们研究深度神经网络在抽象视觉推理（AVR）领域的泛化与知识复用能力，采用公认的AVR能力评估基准任务——瑞文渐进矩阵（RPMs）开展研究，围绕I-RAVEN数据集探究了两种知识迁移场景。首先，受PGM数据集的泛化评估能力与I-RAVEN的普及性启发，我们提出无属性I-RAVEN（A-I-RAVEN），该基准包含10种泛化模式，可系统测试抽象规则在不同复杂度级别（基础模式与扩展模式）下应用于未见属性的泛化效果。与PGM相比，A-I-RAVEN具备组合性、图形配置多样的特点，且无需大量计算资源。其次，我们构建了I-RAVEN-Mesh数据集，通过包含线条图案的新型组件结构丰富RPMs，便于评估迁移学习场景下的渐进式知识获取能力。我们在新提出的数据集上评估了AVR领域文献中的13个强模型，揭示了它们在泛化与知识迁移方面的具体缺陷。
英文摘要：
We study generalization and knowledge reuse capabilities of deep neural networks in the domain of abstract visual reasoning (AVR), employing Raven's Progressive Matrices (RPMs), a recognized benchmark task for assessing AVR abilities. Two knowledge transfer scenarios referring to the I-RAVEN dataset are investigated. Firstly, inspired by generalization assessment capabilities of the PGM dataset and popularity of I-RAVEN, we introduce Attributeless-I-RAVEN (A-I-RAVEN), a benchmark with 10 generalization regimes that allow to systematically test generalization of abstract rules applied to held-out attributes at various levels of complexity (primary and extended regimes). In contrast to PGM, A-I-RAVEN features compositionality, a variety of figure configurations, and does not require substantial computational resources. Secondly, we construct I-RAVEN-Mesh, a dataset that enriches RPMs with a novel component structure comprising line-based patterns, facilitating assessment of progressive knowledge acquisition in transfer learning setting. We evaluate 13 strong models from the AVR literature on the introduced datasets, revealing their specific shortcomings in generalization and knowledge transfer.
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