360◦ videos convey holistic views for the surroundings of a scene. It provides audio-visual cues beyond predetermined 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◦ 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.
|Title of host publication||Proceedings - 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||11|
|Publication status||Published - 2021|
|Event||18th IEEE/CVF International Conference on Computer Vision, ICCV 2021 - Virtual, Online, Canada|
Duration: 2021 Oct 11 → 2021 Oct 17
|Name||Proceedings of the IEEE International Conference on Computer Vision|
|Conference||18th IEEE/CVF International Conference on Computer Vision, ICCV 2021|
|Period||21/10/11 → 21/10/17|
Bibliographical noteFunding Information:
Acknowledgement. We thank the anonymous reviewers for their thoughtful suggestions on this work. This work was supported by AIRS Company in Hyundai Motor Company & Kia Corporation through HKMC-SNU AI Consortium Fund, Brain Research Program by National Research Foundation of Korea (NRF) (2017M3C7A1047860) and Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2019-0-01082, SW StarLab). Gunhee Kim is the corresponding author.
© 2021 IEEE
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition