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:
this https URL

本篇论文提出了一种行动识别的泛化问题，使用介绍了包含大型 Ego4D 数据集中的 1.1M 视频剪辑的 ARGO1M 数据集，对 10 个场景和 13 个位置进行分类。作者提出了一种名为 CIR 的方法，通过复制其他领域的视频的重建，并利用文本叙述来指导域可推广表示的学习，结果表明 CIR 在 ARGO1M 的所有测试中均优于先前的域泛化方法。