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VideoCon: Robust Video-Language Alignment via Contrast Captions by Bansal, Hritik; Bitton, Yonatan; Szpektor, Idan; Chang, Kai-Wei; Grover, Aditya is a scholarly article available to read on EtoBox.

What is VideoCon: Robust Video-Language Alignment via Contrast Captions about?

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 no

Author

Bansal, Hritik; Bitton, Yonatan; Szpektor, Idan; Chang, Kai-Wei; Grover, Aditya

Published

2023

Language

EN