PR-Net: Preference Reasoning for Personalized Video Highlight Detection

• URL: http://arxiv.org/abs/2109.01799v1
• Date: Sat, 4 Sep 2021 06:12:13 GMT
• Title: PR-Net: Preference Reasoning for Personalized Video Highlight Detection
• Authors: Runnan Chen, Penghao Zhou, Wenzhe Wang, Nenglun Chen, Pai Peng, Xing Sun, Wenping Wang
• Abstract summary: We propose a simple yet efficient preference reasoning framework (PR-Net) to explicitly take the diverse interests into account for frame-level highlight prediction. Our method significantly outperforms state-of-the-art methods with a relative improvement of 12% in mean accuracy precision.
• Score: 34.71807317380797
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Personalized video highlight detection aims to shorten a long video to interesting moments according to a user's preference, which has recently raised the community's attention. Current methods regard the user's history as holistic information to predict the user's preference but negating the inherent diversity of the user's interests, resulting in vague preference representation. In this paper, we propose a simple yet efficient preference reasoning framework (PR-Net) to explicitly take the diverse interests into account for frame-level highlight prediction. Specifically, distinct user-specific preferences for each input query frame are produced, presented as the similarity weighted sum of history highlights to the corresponding query frame. Next, distinct comprehensive preferences are formed by the user-specific preferences and a learnable generic preference for more overall highlight measurement. Lastly, the degree of highlight and non-highlight for each query frame is calculated as semantic similarity to its comprehensive and non-highlight preferences, respectively. Besides, to alleviate the ambiguity due to the incomplete annotation, a new bi-directional contrastive loss is proposed to ensure a compact and differentiable metric space. In this way, our method significantly outperforms state-of-the-art methods with a relative improvement of 12% in mean accuracy precision.

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