Audio-Visual Generalized Zero-Shot Learning using Pre-Trained Large Multi-Modal Models
<|reference_start|>Audio-Visual Generalized Zero-Shot Learning using Pre-Trained Large Multi-Modal Models: Audio-visual zero-shot learning methods commonly build on features extracted from pre-trained models, e.g. video or audio classification models. However, existing benchmarks predate the popularization of large multi-modal models, such as CLIP and CLAP. In this work, we explore such large pre-trained models to obtain features, i.e. CLIP for visual features, and CLAP for audio features. Furthermore, the CLIP and CLAP text encoders provide class label embeddings which are combined to boost the performance of the system. We propose a simple yet effective model that only relies on feed-forward neural networks, exploiting the strong generalization capabilities of the new audio, visual and textual features. Our framework achieves state-of-the-art performance on VGGSound-GZSL, UCF-GZSL, and ActivityNet-GZSL with our new features. Code and data available at: https://github.com/dkurzend/ClipClap-GZSL.<|reference_end|>
@article{kurzendörfer2024audio-visual,
    title={Audio-Visual Generalized Zero-Shot Learning using Pre-Trained Large
  Multi-Modal Models},
    author={David Kurzend"orfer and Otniel-Bogdan Mercea and A. Sophia Koepke and
  Zeynep Akata},
    journal={arXiv preprint arXiv:2404.06309},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.06309},
    primaryClass={cs.CV}
}