  We propose and address a new generalisation problem: can a model trained for
action recognition successfully classify actions when they are performed within
a previously unseen scenario and in a previously unseen location? To answer
this question, we introduce the Action Recognition Generalisation Over
scenarios and locations dataset (ARGO1M), which contains 1.1M video clips from
the large-scale Ego4D dataset, across 10 scenarios and 13 locations. We
demonstrate recognition models struggle to generalise over 10 proposed test
splits, each of an unseen scenario in an unseen location. We thus propose CIR,
a method to represent each video as a Cross-Instance Reconstruction of videos
from other domains. Reconstructions are paired with text narrations to guide
the learning of a domain generalisable representation. We provide extensive
analysis and ablations on ARGO1M that show CIR outperforms prior domain
generalisation works on all test splits. Code and data:
https://chiaraplizz.github.io/what-can-a-cook/.
