  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
