AutoTVG: A New Vision-language Pre-training Paradigm for Temporal Video Grounding

TL;DR

AutoTVG introduces a novel pre-training paradigm for temporal video grounding that leverages automatically annotated untrimmed videos to improve zero-shot and supervised performance.

Contribution

It proposes AutoTVG, a new paradigm with a captioned moment generation module and a regression-based grounding network, addressing limitations of traditional pre-training methods.

Findings

Achieves competitive zero-shot performance on Charades-STA and ActivityNet Captions.

Outperforms existing pre-training frameworks with less training data.

Effectively learns semantic alignment and boundary regression from unannotated videos.

Abstract

Temporal Video Grounding (TVG) aims to localize a moment from an untrimmed video given the language description. Since the annotation of TVG is labor-intensive, TVG under limited supervision has accepted attention in recent years. The great success of vision-language pre-training guides TVG to follow the traditional "pre-training + fine-tuning" paradigm, however, the pre-training process would suffer from a lack of temporal modeling and fine-grained alignment due to the difference of data nature between pre-train and test. Besides, the large gap between pretext and downstream tasks makes zero-shot testing impossible for the pre-trained model. To avoid the drawbacks of the traditional paradigm, we propose AutoTVG, a new vision-language pre-training paradigm for TVG that enables the model to learn semantic alignment and boundary regression from automatically annotated untrimmed videos. To be specific, AutoTVG consists of a novel Captioned Moment Generation (CMG) module to generate captioned moments from untrimmed videos, and TVGNet with a regression head to predict localization results. Experimental results on Charades-STA and ActivityNet Captions show that, regarding zero-shot temporal video grounding, AutoTVG achieves highly competitive performance with in-distribution methods under out-of-distribution testing, and is superior to existing pre-training frameworks with much less training data.