# VindLU: A Recipe for Effective Video-and-Language Pretraining

**Authors:** Feng Cheng, Xizi Wang, Jie Lei, David Crandall, Mohit Bansal, Gedas Bertasius  
**arXiv:** [2212.05051](https://arxiv.org/abs/2212.05051)  
**Date:** 2022-12-09  
**Summary page:** https://www.summarizepaper.com/en/paper/2212.05051v1/

## Key points

- Significant progress in video-and-language (VidL) understanding in recent years
- Empirical study conducted to demystify important factors in VidL model design
- Factors investigated include spatiotemporal architecture design, multimodal fusion schemes, pretraining objectives, choice of pretraining data, pretraining and finetuning protocols, and dataset and model scaling
- Results reveal that temporal modeling, video-to-text multimodal fusion, masked modeling objectives, and joint training on images and videos are critical for good performance on VidL tasks
- Step-by-step recipe called VindLU developed based on insights for effective VidL pretraining
- VindLU achieves comparable or better results than state-of-the-art approaches on several VidL tasks without relying on external CLIP pretraining
- Approach outperforms current state-of-the-art by 7.8% on DiDeMo and 6.1% on ActivityNet for text-to-video retrieval task
- Complexity of modern VidL frameworks highlighted by dissecting them along multiple dimensions such as temporal modeling schemes, multimodal fusion modules, pretraining objectives source of pretraining data, and number of frames used for pretraining
- Existing studies focused mainly on image-based analysis or specific aspects of video modelling but did not cover a broad range of VidL design factors like this work does
- Provides valuable empirical insights into VidL framework design choices and offers a practical recipe for effective VidL pretraining

## Summary

In recent years, there has been significant progress in video-and-language (VidL) understanding. This paper conducts an empirical study to demystify the most important factors in VidL model design. The study investigates various factors including spatiotemporal architecture design, multimodal fusion schemes, pretraining objectives, choice of pretraining data, pretraining and finetuning protocols, and dataset and model scaling. The results reveal that temporal modeling, video-to-text multimodal fusion, masked modeling objectives, and joint training on images and videos are critical for good performance on VidL tasks. Based on these insights, the authors develop a step-by-step recipe called VindLU for effective VidL pretraining. The final model trained using this recipe achieves comparable or better results than state-of-the-art approaches on several VidL tasks without relying on external CLIP pretraining. For example, on the text-to-video retrieval task their approach outperforms current state-of-the art by 7.8% on DiDeMo and 6.1% on ActivityNet. The paper also highlights the complexity of modern VidL frameworks by dissecting them along multiple dimensions such as temporal modeling schemes, multimodal fusion modules, pretraining objectives source of pretraining data and number of frames used for pretraining. It is noted that existing studies focused mainly on image based analysis or specific aspects of video modelling but did not cover a broad range of VidL design factors like this work does. Overall this work provides valuable empirical insights into VidL framework design choices and offers a practical recipe for effective VidL pre training . The findings contribute to advancing research in the VidL domain.