[30] Rui Qian, Di Hu, Heinrich Dinkel, Mengyue Wu, Ning Xu, and Weiyao Lin. Multiple sound sources localization from coarse to fine. In Proceedings of the European Conference on Computer Vision (ECCV), pages 292-308, 2020.
[31] Hanyu Xuan, Zhiliang Wu, Jian Yang, Yan Yan, and Xavier Alameda-Pineda. A proposal-based paradigm for self-supervised sound source localization in videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1029-1038, 2022.
[32] Fanyi Xiao, Yong Jae Lee, Kristen Grauman, Jitendra Malik, and Christoph Feichtenhofer. Audiovisual slowfast networks for video recognition. arXiv preprint arXiv:2001.08740, 2020.
[33] Ruohan Gao, Tae-Hyun Oh, Kristen Grauman, and Lorenzo Torresani. Listen to look: Action recognition by previewing audio. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10457-10467, 2020.
[34] Rameswar Panda, Chun-Fu Richard Chen, Quanfu Fan, Xineng Sun, Kate Saenko, Aude Oliva, and Rogerio Feris. Adamml: Adaptive multi-modal learning for efficient video recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7576-7585, 2021.
[35] Saghir Alfasly, Jian Lu, Chen Xu, and Yuru Zou. Learnable irrelevant modality dropout for multimodal action recognition on modality-specific annotated videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20208-20217, 2022.
[36] Shentong Mo and Yapeng Tian. Multi-modal grouping network for weakly-supervised audiovisual video parsing. In Advances in Neural Information Processing Systems, 2022.
[37] Armand Joulin, Laurens Van Der Maaten, Allan Jabri, and Nicolas Vasilache. Learning visual features from large weakly supervised data. In Proceedings of the European Conference on Computer Vision (ECCV), pages 67-84, 2016.
[38] Ang Li, Allan Jabri, Armand Joulin, and Laurens Van Der Maaten. Learning visual n-grams from web data. In Proceedings of the IEEE International Conference on Computer Vision, pages 4183–4192, 2017.
[39] Bart Thomee, David A Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li. Yfcc100m: The new data in multimedia research. Communications of the ACM, (2):64-73, 2016.
[40] Mert Bulent Sariyildiz, Julien Perez, and Diane Llarus. Learning visual representations with caption annotations. In Proceedings of the European Conference on Computer Vision (ECCV), pages 153-170, 2020.
[41] Karan Desai and Justin Johnson. Virtex: Learning visual representations from textual annotations. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11162-11173, 2021.
[42] Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, and C Lawrence Zitnick. Microsoft coco captions: Data collection and evaluation server. arXiv preprint arXiv:1504.00325, 2015.
[43] Manuele Barraco, Marcella Cornia, Silvia Cascianelli, Lorenzo Baraldi, and Rita Cucchiara. The unreasonable effectiveness of clip features for image captioning: an experimental analysis. In proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4662-4670, 2022.
[44] Mingkang Tang, Zhanyu Wang, Zhenhua Liu, Fengyun Rao, Dian Li, and Xiu Li. Clip4caption: Clip for video caption. In Proceedings of the 29th ACM International Conference on Multimedia, pages 4858-4862, 2021.
[45] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.