[19] Andrey Guzhov, Federico Raue, Jörn Hees, and Andreas Dengel. Audioclip: Extending clip to image, text and audio. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 976-980. IEEE, 2022.
[20] Ho-Hsiang Wu, Prem Seetharaman, Kundan Kumar, and Juan Pablo Bello. Wav2clip: Learning robust audio representations from clip. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 4563-4567. IEEE, 2022.
[21] Jort F Gemmeke, Daniel PW Ellis, Dylan Freedman, Aren Jansen, Wade Lawrence, R Channing Moore, Manoj Plakal, and Marvin Ritter. Audio set: An ontology and human-labeled dataset for audio events. In 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP), pages 776-780. IEEE, 2017.
[22] Honglie Chen, Weidi Xie, Andrea Vedaldi, and Andrew Zisserman. Vggsound: A large-scale audio-visual dataset. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 721-725. IEEE, 2020.
[23] Hao Zhu, Man-Di Luo, Rui Wang, Ai-Hua Zheng, and Ran He. Deep audio-visual learning: A survey. International Journal of Automation and Computing, 18:351–376, 2021.
[24] Donghuo Zeng, Yanan Wang, Jianming Wu, and Kazushi Ikeda. Complete cross-triplet loss in label space for audio-visual cross-modal retrieval. arXiv preprint arXiv:2211.03434, 2022.
[25] Donghuo Zeng, Yi Yu, and Keizo Oyama. Deep triplet neural networks with cluster-cca for audio-visual cross-modal retrieval. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 16(3):1-23, 2020.
[26] Luís Vilaça, Yi Yu, and Paula Viana. Recent advances and challenges in deep audio-visual correlation learning. arXiv preprint arXiv:2202.13673, 2022.
[27] Yan-Bo Lin, Yi-Lin Sung, Jie Lei, Mohit Bansal, and Gedas Bertasius. Vision transformers are parameter-efficient audio-visual learners. arXiv preprint arXiv:2212.07983, 2022.
[28] Yang Zhao, Chen Zhang, Haifeng Huang, Haoyuan Li, and Zhou Zhao. Towards effective multi-modal interchanges in zero-resource sounding object localization. Advances in Neural Information Processing Systems, 35:38089-38102, 2022.
[29] Rui Qian, Di Hu, Heinrich Dinkel, Mengyue Wu, Ning Xu, and Weiyao Lin. Multiple sound sources localization from coarse to fine. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XX 16, pages 292-308. Springer, 2020.
[30] Triantafyllos Afouras, Andrew Owens, Joon Son Chung, and Andrew Zisserman. Self-supervised learning of audio-visual objects from video. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XVIII 16, pages 208–224. Springer, 2020.
[31] Di Hu, Feiping Nie, and Xuelong Li. Deep multimodal clustering for unsupervised audiovisual learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9248-9257, 2019.
[32] Arda Senocak, Tae-Hyun Oh, Junsik Kim, Ming-Hsuan Yang, and In So Kweon. Learning to localize sound source in visual scenes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4358-4366, 2018.
[33] Di Hu, Rui Qian, Minyue Jiang, Xiao Tan, Shilei Wen, Errui Ding, Weiyao Lin, and Dejing Dou. Discriminative sounding objects localization via self-supervised audiovisual matching. Advances in Neural Information Processing Systems, 33:10077-10087, 2020.
[34] Xian Liu, Rui Qian, Hang Zhou, Di Hu, Weiyao Lin, Ziwei Liu, Bolei Zhou, and Xiaowei Zhou. Visual sound localization in the wild by cross-modal interference erasing. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 1801-1809, 2022.
[35] Arda Senocak, Hyeonggon Ryu, Junsik Kim, and In So Kweon. Learning sound localization better from semantically similar samples. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 4863-4867. IEEE, 2022.
[36] Shentong Mo and Pedro Morgado. Localizing visual sounds the easy way. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXXVII, pages 218-234. Springer, 2022.