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

TL;DR

This paper demonstrates that transferring knowledge from pretrained language models to vision-based models significantly improves video action anticipation performance, achieving state-of-the-art results.

Contribution

It introduces a simple cross-modal distillation method to incorporate textual knowledge into vision models for action anticipation.

Findings

Achieved 3.5% relative gain on EGTEA-GAZE+ dataset.

Achieved 7.2% relative gain on EPIC-KITCHEN 55 dataset.

Set new state-of-the-art results in video action anticipation.

Abstract

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.