EgoVLPv2: Egocentric Video-Language Pre-training with Fusion in the Backbone

TL;DR

EgoVLPv2 introduces a novel egocentric video-language pre-training framework that integrates cross-modal fusion directly into the backbone, enabling efficient, flexible, and state-of-the-art performance across diverse vision-language tasks.

Contribution

It incorporates lightweight cross-modal fusion into the backbone, improving pre-training and reducing fine-tuning costs compared to previous methods.

Findings

Achieves state-of-the-art results on multiple VL tasks.

Demonstrates efficient fusion strategy with reduced computational overhead.

Supports diverse downstream tasks with a unified model.

Abstract

Video-language pre-training (VLP) has become increasingly important due to its ability to generalize to various vision and language tasks. However, existing egocentric VLP frameworks utilize separate video and language encoders and learn task-specific cross-modal information only during fine-tuning, limiting the development of a unified system. In this work, we introduce the second generation of egocentric video-language pre-training (EgoVLPv2), a significant improvement from the previous generation, by incorporating cross-modal fusion directly into the video and language backbones. EgoVLPv2 learns strong video-text representation during pre-training and reuses the cross-modal attention modules to support different downstream tasks in a flexible and efficient manner, reducing fine-tuning costs. Moreover, our proposed fusion in the backbone strategy is more lightweight and compute-efficient than stacking additional fusion-specific layers. Extensive experiments on a wide range of VL tasks demonstrate the effectiveness of EgoVLPv2 by achieving consistent state-of-the-art performance over strong baselines across all downstream. Our project page can be found at https://shramanpramanick.github.io/EgoVLPv2/.