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FitCLIP: Refining Large-Scale Pretrained Image-Text Models for Zero-Shot Video Understanding Tasks

2022-03-24 22:35:00
Santiago Castro, Fabian Caba Heilbron

Abstract

Large-scale pretrained image-text models have shown incredible zero-shot performance in a handful of tasks, including video ones such as action recognition and text-to-video retrieval. However, these models haven't been adapted to video, mainly because they don't account for the time dimension but also because video frames are different from the typical images (e.g., containing motion blur, less sharpness). In this paper, we present a fine-tuning strategy to refine these large-scale pretrained image-text models for zero-shot video understanding tasks. We show that by carefully adapting these models we obtain considerable improvements on two zero-shot Action Recognition tasks and three zero-shot Text-to-video Retrieval tasks. The code is available at this https URL

Abstract (translated)

URL

https://arxiv.org/abs/2203.13371

PDF

https://arxiv.org/pdf/2203.13371.pdf


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