[1] Tom Brown et al. "Language models are few-shot learners". In: Advances in neural information processing systems 33 (2020), pp. 1877-1901.
[2] Honglie Chen et al. "Vggsound: A large-scale audio-visual dataset". In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. 2020, pp. 721-725.
[3] Ke Chen et al. "HTS-AT: A hierarchical token-semantic audio transformer for sound classification and detection". In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. 2022, pp. 646-650.
[4] Alexey Dosovitskiy et al. "An image is worth 16x16 words: Transformers for image recognition at scale". In: arXiv preprint arXiv:2010.11929 (2020).
[5] Benjamin Elizalde et al. "Clap: Learning audio concepts from natural language supervision". In: arXiv preprint arXiv:2206.04769 (2022).
[6] Mark Everingham et al. "The Pascal visual object classes (voc) challenge". In: International journal of computer vision 88 (2010), pp. 303-338.
[7] Peng Gao et al. "Clip-adapter: Better vision-language models with feature adapters". In: arXiv preprint arXiv:2110.04544 (2021).
[8] Ruohan Gao and Kristen Grauman. "Visualvoice: Audio-visual speech separation with cross-modal consistency". In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE. 2021, pp. 15490–15500.
[9] Jort F Gemmeke et al. "Audio set: An ontology and human-labeled dataset for audio events". In: 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE. 2017, pp. 776-780.
[10] Andrey Guzhov et al. "Audioclip: Extending clip to image, text and audio". In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. 2022, pp. 976-980.
[11] Jie Hu, Li Shen, and Gang Sun. "Squeeze-and-excitation networks". In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018, pp. 7132-7141.
[12] Muhammad Uzair Khattak et al. "Maple: Multi-modal prompt learning". In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023, pp. 19113-19122.
[13] Brian Lester, Rami Al-Rfou, and Noah Constant. "The power of scale for parameter-efficient prompt tuning". In: arXiv preprint arXiv:2104.08691 (2021).
[14] Guangyao Li et al. "Learning to answer questions in dynamic audio-visual scenarios". In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022, pp. 19108-19118.
[15] Xiang Lisa Li and Percy Liang. "Prefix-tuning: Optimizing continuous prompts for generation". In: arXiv preprint arXiv:2101.00190 (2021).
[16] Yan-Bo Lin, Yu-Jhe Li, and Yu-Chiang Frank Wang. "Dual-modality seq2seq network for audio-visual event localization". In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. 2019, pp. 2002-2006.
[17] Yan-Bo Lin et al. "Vision Transformers Are Parameter-Efficient Audio-Visual Learners". In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). June 2023, pp. 2299–2309.
[18] Xiao Liu et al. "GPT understands, too". In: arXiv preprint arXiv:2103.10385 (2021).
[19] Xiao Liu et al. "P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks". In: arXiv preprint arXiv:2110.07602 (2021).
[20] Ze Liu et al. "Swin transformer: Hierarchical vision transformer using shifted windows". In: Proceedings of the IEEE/CVF international conference on computer vision. 2021, pp. 10012-10022.
[21] Yan Luo et al. "Timestamps as Prompts for Geography-Aware Location Recommendation". In: arXiv preprint arXiv:2304.04151 (2023).
[22] Shuang Ma et al. "Active contrastive learning of audio-visual video representations". In: arXiv preprint arXiv:2009.09805 (2020).