  Context is of fundamental importance to both human and machine vision -- 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 works
have crowd-sourced out-of-context photographs from the web to study scene
context, controlling the nature and extent of contextual violations has been an
extremely 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 then conduct a series of experiments to gain insights into the
impact of contextual cues on both human and machine vision using OCD. First, we
conduct psycho-physics experiments to establish a human benchmark for
out-of-context recognition, and then compare it with state-of-the-art computer
vision models to quantify the gap between the two. Finally, 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 significantly better
robustness in out-of-context conditions compared to baseline models across OCD
and other existing out-of-context natural image datasets. All source code and
data are publicly available https://github.com/kreimanlab/WhenPigsFlyContext.
