VideoCon: Robust Video-Language Alignment via Contrast Captions
This addresses robustness issues in video-language alignment for applications like retrieval and QA, though it is incremental as it builds on existing models with a new dataset and fine-tuning approach.
The paper tackles the problem of video-language alignment models lacking robustness to semantically-plausible contrastive changes in captions, and introduces VideoCon, a dataset and model that significantly improves performance, achieving a 12-point increase in AUC on human-generated contrast captions and setting new state-of-the-art in zero-shot tasks like text-to-video retrieval and video question answering.
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.