超越任务特定推理：一个用于抽象视觉推理的统一条件生成框架
Beyond Task-Specific Reasoning: A Unified Conditional Generative Framework for Abstract Visual Reasoning
AI总结：
本文提出统一条件生成求解器（UCGS），将多个抽象视觉推理任务转化为估计目标图像可预测性问题，通过单轮多任务训练一个条件生成模型即可解决多种任务，并具备零样本推理能力。
摘要：
抽象视觉推理（AVR）使人类能够快速发现抽象规则并将其泛化到新场景。设计具有类人AVR能力的人工智能系统一直是人工智能领域的长期课题。深度AVR求解器近期在各类AVR任务中取得了显著成功。然而，它们通常在不同任务中使用任务特定的设计或参数。在这种范式下，解决新任务往往意味着重新训练模型，有时甚至需要重新调整模型架构，这增加了解决AVR问题的成本。与任务特定方法不同，本文提出了一种新颖的统一条件生成求解器（UCGS），旨在以统一框架解决多个AVR任务。首先，我们证明一些知名的AVR任务可以被重新表述为估计问题面板中目标图像的可预测性问题。然后，我们阐明，在所提出的框架下，训练一个条件生成模型即可解决各种AVR任务。实验表明，仅需一轮多任务训练，UCGS就能在多种AVR任务中展现出抽象推理能力。特别地，UCGS表现出零样本推理能力，使其能在测试阶段对来自未见AVR任务的问题进行抽象推理。
英文摘要：
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.
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。
