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Anticipating future actions in a video is useful for many autonomous andassistive technologies. Most prior action anticipation work treat this as avision modality problem, where the models learn the task information primarilyfrom the video features in the action anticipation datasets. However, knowledgeabout action sequences can also be obtained from external textual data. In thiswork, we show how knowledge in pretrained language models can be adapted anddistilled into vision-based action anticipation models. We show that a simpledistillation technique can achieve effective knowledge transfer and provideconsistent gains on a strong vision model (Anticipative Vision Transformer) fortwo action anticipation datasets (3.5% relative gain on EGTEA-GAZE+ and 7.2%relative gain on EPIC-KITCHEN 55), giving a new state-of-the-art result.

Text-Derived Knowledge Helps Vision: A Simple Cross-modal Distillation for Video-based Action Anticipation