Video Understanding as Machine Translation

TL;DR

This paper introduces a generative, translation-based approach to video understanding that eliminates the need for negative sampling, enabling a unified framework for various tasks with improved performance on multiple benchmarks.

Contribution

It proposes a novel generative modeling framework for video understanding that removes the reliance on negative sampling and contrastive learning, unifying multiple tasks.

Findings

Achieved state-of-the-art results on several downstream tasks.

Demonstrated effectiveness on large-scale HowTo100M dataset.

Improved performance in video classification, question answering, captioning, and retrieval.

Abstract

With the advent of large-scale multimodal video datasets, especially sequences with audio or transcribed speech, there has been a growing interest in self-supervised learning of video representations. Most prior work formulates the objective as a contrastive metric learning problem between the modalities. To enable effective learning, however, these strategies require a careful selection of positive and negative samples often combined with hand-designed curriculum policies. In this work we remove the need for negative sampling by taking a generative modeling approach that poses the objective as a translation problem between modalities. Such a formulation allows us to tackle a wide variety of downstream video understanding tasks by means of a single unified framework, without the need for large batches of negative samples common in contrastive metric learning. We experiment with the large-scale HowTo100M dataset for training, and report performance gains over the state-of-the-art on several downstream tasks including video classification (EPIC-Kitchens), question answering (TVQA), captioning (TVC, YouCook2, and MSR-VTT), and text-based clip retrieval (YouCook2 and MSR-VTT).