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arXiv · 2212.04979

VideoCoCa: Video-Text Modeling with Zero-Shot Transfer from Contrastive Captioners

Shen Yan·Tao Zhu·Zirui Wang·Yuan Cao·Mi Zhang·Soham Ghosh·Yonghui Wu·Jiahui Yu
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Abstract

We explore an efficient approach to establish a foundational video-text model. We present VideoCoCa that maximally reuses a pretrained image-text contrastive captioner (CoCa) model and adapt it to video-text tasks with minimal extra training. While previous works adapt image-text models with various cross-frame fusion modules, we find that the generative attentional pooling and contrastive attentional pooling layers in CoCa are instantly adaptable to flattened frame embeddings, yielding state-of-the-art results on zero-shot video classification and zero-shot text-to-video retrieval. Furthermore, we explore lightweight finetuning on top of VideoCoCa, and achieve strong results on video question-answering and video captioning.

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Shen Yan, Tao Zhu, Zirui Wang, Yuan Cao, Mi Zhang, Soham Ghosh, Yonghui Wu, Jiahui Yu. 2022-12-09. VideoCoCa: Video-Text Modeling with Zero-Shot Transfer from Contrastive Captioners. https://arxiv.org/abs/2212.04979

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