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We introduce a zero-shot video captioning method that employs two frozennetworks: the GPT-2 language model and the CLIP image-text matching model. Thematching score is used to steer the language model toward generating a sentencethat has a high average matching score to a subset of the video frames. Unlikezero-shot image captioning methods, our work considers the entire sentence atonce. This is achieved by optimizing, during the generation process, part ofthe prompt from scratch, by modifying the representation of all other tokens inthe prompt, and by repeating the process iteratively, gradually improving thespecificity and comprehensiveness of the generated sentence. Our experimentsshow that the generated captions are coherent and display a broad range ofreal-world knowledge. Our code is available at:https://github.com/YoadTew/zero-shot-video-to-text

Zero-Shot Video Captioning with Evolving Pseudo-Tokens