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InstaIndoor and multi-modal deep learning for indoor scene recognition

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Abstract

Indoor scene recognition is a growing field with great potential for behaviour understanding, robot localization, and elderly monitoring, among others. In this study, we approach the task of scene recognition from a novel standpoint, using multi-modal learning and video data gathered from social media. The accessibility and variety of social media videos can provide realistic data for modern scene recognition techniques and applications. We propose a model based on fusion of transcribed speech to text and visual features, which is used for classification on a novel dataset of social media videos of indoor scenes named InstaIndoor. Our model achieves up to 70% accuracy and 0.7 F1-Score. Furthermore, we highlight the potential of our approach by benchmarking on a YouTube-8M subset of indoor scenes as well, where it achieves 74% accuracy and 0.74 F1-Score. We hope the contributions of this work pave the way to novel research in the challenging field of indoor scene recognition.

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Notes

  1. Both datasets and corresponding pipeline code are available at https://github.com/andreea-glavan/multimodal-audiovisual-scene-recognition.

  2. Both datasets and corresponding pipeline code are available at https://github.com/andreea-glavan/multimodal-audiovisual-scene-recognition.

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Acknowledgements

We would like to thank the Center for Information Technology of the University of Groningen for their support and for providing access to the Peregrine high performance computing cluster.

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  1. Department of Computer Science, University of Groningen, Nijenborgh 9, 9747, AG, Groningen, Netherlands

    Andreea Glavan & Estefanía Talavera

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  1. Andreea Glavan
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Glavan, A., Talavera, E. InstaIndoor and multi-modal deep learning for indoor scene recognition. Neural Comput & Applic 34, 6861–6877 (2022). https://doi.org/10.1007/s00521-021-06781-2

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