Joint learning of images and videos with a single Vision Transformer
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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Datasets
<h2>oi</h2>
CIFAR-100
UCF101
Kinetics
Kinetics 400
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