Despite being (pre)trained on a massive amount of data, state-of-the-art video-language alignment models are not robust to semantically-plausible contrastive changes in the video captions. Our work addresses this by identifying a broad spectrum of contrast misalignments, such as replacing entities, actions, and flipping event order, which alignment models should be robust against. To this end, we introduce the VideoCon, a video-language alignment dataset constructed by a large language model that generates plausible contrast video captions and explanations for differences between original and contrast video captions. Then, a generative video-language model is finetuned with VideoCon to assess video-language entailment and generate explanations. Our VideoCon-based alignment model significantly outperforms current models. It exhibits a 12-point increase in AUC for the video-language alignment task on human-generated contrast captions. Finally, our model sets new state of the art zero-shot performance in temporally-extensive video-language tasks such as text-to-video retrieval (SSv2-Temporal) and video question answering (ATP-Hard). Moreover, our model shows superior performance on novel videos and human-crafted captions and explanations. Our code and data are available at https://github.com/Hritikbansal/videocon.

通过介绍VideoCon，我们的研究在应对视频字幕中语义上合理的对比变化方面改进了视频-语言对齐模型的鲁棒性，提出了一个广泛的对比失配频谱，并通过一个大规模语言模型构建了基于对比视频字幕的VideoCon对齐数据集，我们的对齐模型在人为生成的对比字幕上在视频-语言对齐任务中的AUC指标上有了12个百分点的提升，同时在诸如文本到视频检索（SSv2-Temporal）和视频问答（ATP-Hard）等时间广泛的视频-语言任务中表现出了零-shot的最新性能。

VideoCon: 强大的视频-语言对齐通过对比字幕