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360$^\circ$ videos convey holistic views for the surroundings of a scene. Itprovides audio-visual cues beyond pre-determined normal field of views anddisplays distinctive spatial relations on a sphere. However, previous benchmarktasks for panoramic videos are still limited to evaluate the semanticunderstanding of audio-visual relationships or spherical spatial property insurroundings. We propose a novel benchmark named Pano-AVQA as a large-scalegrounded audio-visual question answering dataset on panoramic videos. Using5.4K 360$^\circ$ video clips harvested online, we collect two types of novelquestion-answer pairs with bounding-box grounding: spherical spatial relationQAs and audio-visual relation QAs. We train several transformer-based modelsfrom Pano-AVQA, where the results suggest that our proposed spherical spatialembeddings and multimodal training objectives fairly contribute to a bettersemantic understanding of the panoramic surroundings on the dataset.

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