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/.

翻译：视频-语言预训练（VLP）因其能够泛化至多种视觉与语言任务而变得日益重要。然而，现有的第一人称VLP框架采用分离的视频和语言编码器，仅在微调阶段学习特定任务的跨模态信息，限制了统一系统的发展。在本工作中，我们引入了第二代第一人称视频-语言预训练（EgoVLPv2），相较于前代实现了显著改进，其核心在于将跨模态融合直接整合到视频和语言骨干网络中。EgoVLPv2在预训练过程中学习强大的视频-文本表示，并通过复用跨模态注意力模块，以灵活高效的方式支持不同的下游任务，从而降低了微调成本。此外，我们提出的骨干网络融合策略相比堆叠额外融合专用层更为轻量且计算高效。在广泛的视频-语言任务上的大量实验证明了EgoVLPv2的有效性，其在所有下游任务中均实现了相较于强基线的一致最新性能。我们的项目页面请见https://shramanpramanick.github.io/EgoVLPv2/。