# VidLA: Video-Language Alignment at Scale

**Authors:** Mamshad Nayeem Rizve, Fan Fei, Jayakrishnan Unnikrishnan, Son Tran, Benjamin Z. Yao, Belinda Zeng, Mubarak Shah, Trishul Chilimbi  
**arXiv:** [2403.14870](https://arxiv.org/abs/2403.14870)  
**Date:** 2024-03-21  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2403.14870v1/

## Key points

- Authors introduce VidLA as a novel approach for large-scale video-language alignment
- VidLA overcomes challenges of capturing short-range and long-range temporal dependencies by using a simpler network architecture with data tokens at different temporal resolutions
- Utilizes pretrained image-text foundation models to enhance performance
- Addresses the lack of semantically aligned large-scale training data by curating the largest video-language dataset to date with improved visual grounding
- Outperforms state-of-the-art methods on multiple retrieval benchmarks, particularly excelling with longer videos while remaining competitive on classification benchmarks
- Accepted for presentation at CVPR 2024, showcasing significant advancements in aligning video content with textual descriptions at scale

## Summary

In their paper titled "VidLA: Video-Language Alignment at Scale," authors Mamshad Nayeem Rizve, Fan Fei, Jayakrishnan Unnikrishnan, Son Tran, Benjamin Z. Yao, Belinda Zeng, Mubarak Shah, and Trishul Chilimbi introduce VidLA as a novel approach for large-scale video-language alignment. The existing methods face two significant challenges that VidLA aims to overcome. Firstly, previous approaches fail to effectively capture both short-range and long-range temporal dependencies. They often rely on complex hierarchical deep network architectures that are difficult to integrate with pretrained image-text foundation models. To address this limitation, the authors propose a simpler network architecture using data tokens operating at different temporal resolutions in a hierarchical manner. This design accounts for the temporally hierarchical nature of videos and allows for better representation extraction at varying temporal scales. By employing a straightforward two-tower architecture, VidLA can leverage pretrained image-text foundation models to enhance overall performance. Secondly,<DateTime>existing video-language alignment works struggle due to the lack of semantically aligned large-scale training data.</DateTime> In response to this challenge, the authors utilize recent Large Language Models (LLMs) to curate the largest video-language dataset to date with improved visual grounding. Unlike conventional video-text datasets containing only short clips,<DateTime>this dataset includes video clips of varying durations</DateTime>to support the extraction of better representations across different temporal scales. Empirical results demonstrate that VidLA outperforms state-of-the-art methods on multiple retrieval benchmarks,<DateTime>particularly excelling with longer videos while remaining competitive on classification benchmarks.</DateTime>This innovative approach not only addresses key limitations in existing techniques but also showcases significant advancements in aligning video content with textual descriptions at scale. Accepted for presentation at CVPR 2024, VidLA represents a valuable contribution to the fields of computer vision and natural language processing by enabling more effective and efficient video-language alignment processes.