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arXiv · 2410.13585

Pseudo Dataset Generation for Out-of-Domain Multi-Camera View Recommendation

Kuan-Ying Lee·Qian Zhou·Klara Nahrstedt
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Abstract

Multi-camera systems are indispensable in movies, TV shows, and other media. Selecting the appropriate camera at every timestamp has a decisive impact on production quality and audience preferences. Learning-based view recommendation frameworks can assist professionals in decision-making. However, they often struggle outside of their training domains. The scarcity of labeled multi-camera view recommendation datasets exacerbates the issue. Based on the insight that many videos are edited from the original multi-camera videos, we propose transforming regular videos into pseudo-labeled multi-camera view recommendation datasets. Promisingly, by training the model on pseudo-labeled datasets stemming from videos in the target domain, we achieve a 68% relative improvement in the model's accuracy in the target domain and bridge the accuracy gap between in-domain and never-before-seen domains.

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Kuan-Ying Lee, Qian Zhou, Klara Nahrstedt. 2024-10-17. Pseudo Dataset Generation for Out-of-Domain Multi-Camera View Recommendation. https://arxiv.org/abs/2410.13585

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