标题： 超越任务特定推理：抽象视觉推理的统一条件生成框架 显示英文标题

标题： Beyond Task-Specific Reasoning: A Unified Conditional Generative Framework for Abstract Visual Reasoning

摘要： 抽象视觉推理（AVR）使人类能够快速发现并概括抽象规则以适应新场景。 在人工智能领域，设计具有类人AVR能力的智能系统一直是一个长期研究的主题。 最近，深度AVR求解器在各种AVR任务中取得了显著的成功。 然而，它们通常在不同任务中使用特定任务的设计或参数。 在这种范式下，解决新任务通常意味着重新训练模型，有时还需要调整模型结构，这增加了解决AVR问题的成本。 与特定任务的方法相反，本文提出了一种新的统一条件生成求解器（UCGS），旨在在一个统一框架中解决多个AVR任务。 首先，我们证明了一些著名的AVR任务可以重新表述为在问题面板中估计目标图像的可预测性的问题。 然后，我们说明在所提出的框架下，训练一个条件生成模型可以解决各种AVR任务。 实验表明，通过一次多任务训练，UCGS在各种AVR任务中表现出抽象推理能力。 特别是，UCGS表现出零样本推理的能力，使其能够在测试阶段对未见过的AVR任务的问题进行抽象推理。 显示英文摘要

摘要： Abstract visual reasoning (AVR) enables humans to quickly discover and generalize abstract rules to new scenarios. Designing intelligent systems with human-like AVR abilities has been a long-standing topic in the artificial intelligence community. Deep AVR solvers have recently achieved remarkable success in various AVR tasks. However, they usually use task-specific designs or parameters in different tasks. In such a paradigm, solving new tasks often means retraining the model, and sometimes retuning the model architectures, which increases the cost of solving AVR problems. In contrast to task-specific approaches, this paper proposes a novel Unified Conditional Generative Solver (UCGS), aiming to address multiple AVR tasks in a unified framework. First, we prove that some well-known AVR tasks can be reformulated as the problem of estimating the predictability of target images in problem panels. Then, we illustrate that, under the proposed framework, training one conditional generative model can solve various AVR tasks. The experiments show that with a single round of multi-task training, UCGS demonstrates abstract reasoning ability across various AVR tasks. Especially, UCGS exhibits the ability of zero-shot reasoning, enabling it to perform abstract reasoning on problems from unseen AVR tasks in the testing phase.