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Text-to-Feature Diffusion for Audio-Visual Few-Shot Learning

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Pattern Recognition (DAGM GCPR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14264))

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Abstract

Training deep learning models for video classification from audio-visual data commonly requires vast amounts of labeled training data collected via a costly process. A challenging and underexplored, yet much cheaper, setup is few-shot learning from video data. In particular, the inherently multi-modal nature of video data with sound and visual information has not been leveraged extensively for the few-shot video classification task. Therefore, we introduce a unified audio-visual few-shot video classification benchmark on three datasets, i.e. the VGGSound-FSL, UCF-FSL, ActivityNet-FSL datasets, where we adapt and compare ten methods. In addition, we propose AV-Diff, a text-to-feature diffusion framework, which first fuses the temporal and audio-visual features via cross-modal attention and then generates multi-modal features for the novel classes. We show that AV-Diff obtains state-of-the-art performance on our proposed benchmark for audio-visual (generalised) few-shot learning. Our benchmark paves the way for effective audio-visual classification when only limited labeled data is available. Code and data are available at https://github.com/ExplainableML/AVDIFF-GFSL.

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Acknowledgements

This work was supported by BMBF FKZ: 01IS18039A, DFG: SFB 1233 TP 17 - project number 276693517, by the ERC (853489 - DEXIM), and by EXC number 2064/1 - project number 390727645. The authors thank the International Max Planck Research School for Intelligent Systems (IMPRS-IS) for supporting O.-B. Mercea and T. Hummel.

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Authors and Affiliations

  1. University of Tübingen, Tübingen, Germany

    Otniel-Bogdan Mercea, Thomas Hummel, A. Sophia Koepke & Zeynep Akata

  2. MPI for Intelligent Systems, Tübingen, Germany

    Zeynep Akata

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  1. Otniel-Bogdan Mercea
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  2. Thomas Hummel
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  3. A. Sophia Koepke
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  4. Zeynep Akata
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Correspondence to Otniel-Bogdan Mercea .

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  1. IWR, Heidelberg University, Heidelberg, Germany

    Ullrich Köthe

  2. IWR, Heidelberg University, Heidelberg, Germany

    Carsten Rother

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Mercea, OB., Hummel, T., Koepke, A.S., Akata, Z. (2024). Text-to-Feature Diffusion for Audio-Visual Few-Shot Learning. In: Köthe, U., Rother, C. (eds) Pattern Recognition. DAGM GCPR 2023. Lecture Notes in Computer Science, vol 14264. Springer, Cham. https://doi.org/10.1007/978-3-031-54605-1_32

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