When Pigs Fly：Contextual Reasoning in Synthetic and Natural Scenes（当猪飞时：合成与自然场景中的语境推理）
When Pigs Fly: Contextual Reasoning in Synthetic and Natural Scenes
摘要：
上下文对人类和机器视觉都具有根本的重要性;例如，空中的物体更可能是飞机而不是猪。上下文的丰富概念包括几个方面，包括物理规则，统计同现，相对对象大小等。虽然以前的工作集中在从网络上的群众外包的上下文照片研究场景的背景下，控制上下文的性质和程度的侵犯一直是一项艰巨的任务。在这里，我们介绍了一个多样化的，合成的上下文外数据集（OCD）与细粒度控制场景上下文。通过利用3D模拟引擎，我们系统地控制虚拟家庭环境中36个对象类别的重力，对象同现和相对大小。我们进行了一系列实验，以深入了解使用OCD的上下文线索对人类和机器视觉的影响。我们进行了心理物理学实验，以建立一个人类的断章取义识别基准，然后将其与最先进的计算机视觉模型进行比较，以量化两者之间的差距。本文提出了一种上下文感知的识别Transformer模型，通过多头注意力融合目标和上下文信息。我们的模型捕获了用于上下文推理的有用信息，与OCD和其他上下文外数据集的基线模型相比，在上下文外条件下实现了人类水平的性能和更好的鲁棒性。所有源代码和数据都可以在https://github.com/kreimanlab/WhenPigsFlyContext上公开获取
英文摘要：
Context is of fundamental importance to both human and machine vision; e.g., an object in the air is more likely to be an airplane than a pig. The rich notion of context incorporates several aspects including physics rules, statistical co-occurrences, and relative object sizes, among others. While previous work has focused on crowd-sourced out-of-context photographs from the web to study scene context, controlling the nature and extent of contextual violations has been a daunting task. Here we introduce a diverse, synthetic Out-of-Context Dataset (OCD) with fine-grained control over scene context. By leveraging a 3D simulation engine, we systematically control the gravity, object co-occurrences and relative sizes across 36 object categories in a virtual household environment. We conducted a series of experiments to gain insights into the impact of contextual cues on both human and machine vision using OCD. We conducted psychophysics experiments to establish a human benchmark for out-of-context recognition, and then compared it with state-of-the-art computer vision models to quantify the gap between the two. We propose a context-aware recognition transformer model, fusing object and contextual information via multi-head attention. Our model captures useful information for contextual reasoning, enabling human-level performance and better robustness in out-of-context conditions compared to baseline models across OCD and other out-of-context datasets. All source code and data are publicly available at https://github.com/kreimanlab/WhenPigsFlyContext
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。
