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Detecting key frames in videos is a common problem in many applications such as video classification, action recognition and video summarization. These tasks can be performed more efficiently using only a handful of key frames rather than the full video. Existing key frame detection approaches are mostly designed for supervised learning and require manual labelling of key frames in a large corpus of training data to train the models. Labelling requires human annotators from differentdoi:10.3390/s20236941 pmid:33291759 fatcat:pqiovyqo2baa5cyq37os7hxjmy