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Exploring Fine-Grained Audiovisual Categorization with the SSW60 Dataset

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

We present a new benchmark dataset, Sapsucker Woods 60 (SSW60), for advancing research on audiovisual fine-grained categorization. While our community has made great strides in fine-grained visual categorization on images, the counterparts in audio and video fine-grained categorization are relatively unexplored. To encourage advancements in this space, we have carefully constructed the SSW60 dataset to enable researchers to experiment with classifying the same set of categories in three different modalities: images, audio, and video. The dataset covers 60 species of birds and is comprised of images from existing datasets, and brand new, expert curated audio and video datasets. We thoroughly benchmark audiovisual classification performance and modality fusion experiments through the use of state-of-the-art transformer methods. Our findings show that performance of audiovisual fusion methods is better than using exclusively image or audio based methods for the task of video classification. We also present interesting modality transfer experiments, enabled by the unique construction of SSW60 to encompass three different modalities. We hope the SSW60 dataset and accompanying baselines spur research in this fascinating area.

The first two authors contributed equally. https://github.com/visipedia/ssw60.

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Acknowledgment

Serge Belongie is supported in part by the Pioneer Centre for AI, DNRF grant number P1. These investigations would not be possible without the help of the passionate birding community contributing their knowledge and data to the Macaulay Library; thank you!

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Van Horn, G., Qian, R., Wilber, K., Adam, H., Mac Aodha, O., Belongie, S. (2022). Exploring Fine-Grained Audiovisual Categorization with the SSW60 Dataset. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13668. Springer, Cham. https://doi.org/10.1007/978-3-031-20074-8_16

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