Pano-AVQA: Grounded Audio-Visual Question Answering on 360$^\circ$ Videos

• URL: http://arxiv.org/abs/2110.05122v1
• Date: Mon, 11 Oct 2021 09:58:05 GMT
• Title: Pano-AVQA: Grounded Audio-Visual Question Answering on 360$^\circ$ Videos
• Authors: Heeseung Yun, Youngjae Yu, Wonsuk Yang, Kangil Lee, Gunhee Kim
• Abstract summary: We propose a benchmark named Pano-AVQA as a large-scale grounded audio-visual question answering dataset on panoramic videos. Using 5.4K 360$circ$ video clips harvested online, we collect two types of novel question-answer pairs with bounding-box grounding. We train several transformer-based models from Pano-AVQA, where the results suggest that our proposed spherical spatial embeddings and multimodal training objectives fairly contribute to a better semantic understanding of the panoramic surroundings on the dataset.
• Score: 42.32743253830288
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 360$^\circ$ videos convey holistic views for the surroundings of a scene. It provides audio-visual cues beyond pre-determined normal field of views and displays distinctive spatial relations on a sphere. However, previous benchmark tasks for panoramic videos are still limited to evaluate the semantic understanding of audio-visual relationships or spherical spatial property in surroundings. We propose a novel benchmark named Pano-AVQA as a large-scale grounded audio-visual question answering dataset on panoramic videos. Using 5.4K 360$^\circ$ video clips harvested online, we collect two types of novel question-answer pairs with bounding-box grounding: spherical spatial relation QAs and audio-visual relation QAs. We train several transformer-based models from Pano-AVQA, where the results suggest that our proposed spherical spatial embeddings and multimodal training objectives fairly contribute to a better semantic understanding of the panoramic surroundings on the dataset.

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