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arXiv · 2308.10533

Joint learning of images and videos with a single Vision Transformer

Shuki Shimizu·Toru Tamaki
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Abstract

In this study, we propose a method for jointly learning of images and videos using a single model. In general, images and videos are often trained by separate models. We propose in this paper a method that takes a batch of images as input to Vision Transformer IV-ViT, and also a set of video frames with temporal aggregation by late fusion. Experimental results on two image datasets and two action recognition datasets are presented.

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Shuki Shimizu, Toru Tamaki. 2023-08-21. Joint learning of images and videos with a single Vision Transformer. https://arxiv.org/abs/2308.10533

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