Unsupervised Modality-Transferable Video Highlight Detection with Representation Activation Sequence Learning

arXiv:2403.09401v1 Announce Type: new
Abstract: Identifying highlight moments of raw video materials is crucial for improving the efficiency of editing videos that are pervasive on internet platforms. However, the extensive work of manually labeling footage has created obstacles to applying supervised methods to videos of unseen categories. The absence of an audio modality that contains valuable cues for highlight detection in many videos also makes it difficult to use multimodal strategies. In this paper, we propose a novel model with …

abstract arxiv cs.cv detection editing efficiency highlight however internet labeling materials moments obstacles platforms raw representation type unsupervised video videos work