Text-Derived Knowledge Helps Vision: A Simple Cross-modal Distillation for Video-based Action Anticipation
This addresses the problem of improving action anticipation for autonomous and assistive technologies by incorporating textual knowledge, though it is incremental as it builds on existing vision models.
The paper tackles action anticipation in videos by distilling knowledge from pretrained language models into vision-based models, achieving a 3.5% relative gain on EGTEA-GAZE+ and 7.2% on EPIC-KITCHEN 55, resulting in a new state-of-the-art.
Anticipating future actions in a video is useful for many autonomous and assistive technologies. Most prior action anticipation work treat this as a vision modality problem, where the models learn the task information primarily from the video features in the action anticipation datasets. However, knowledge about action sequences can also be obtained from external textual data. In this work, we show how knowledge in pretrained language models can be adapted and distilled into vision-based action anticipation models. We show that a simple distillation technique can achieve effective knowledge transfer and provide consistent gains on a strong vision model (Anticipative Vision Transformer) for two 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.