[12] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The Pascal visual object classes (voc) challenge. International Journal of Computer Vision (IJCV), 88(2):303-338, 2010. 8
[13] Daniel Fried, Jean-Baptiste Alayrac, Phil Blunsom, Chris Dyer, Stephen Clark, and Aida Nematzadeh. Learning to segment actions from observation and narration. In Annual Meeting of the Association for Computational Linguistics, pages 2569-2588, Online, July 2020. Association for Computational Linguistics. 3
[14] Jiyang Gao, Chen Sun, Zhenheng Yang, and Ram Nevatia. Tall: Temporal activity localization via language query. In Proceedings of the IEEE international conference on computer vision, pages 5267-5275, 2017. 3, 4
[15] Simon Ging, Mohammadreza Zolfaghari, Hamed Pirsiavash, and Thomas Brox. Coot: Cooperative hierarchical transformer for video-text representation learning. Advances in neural information processing systems, 33:22605-22618, 2020. 3
[16] Tengda Han, Jue Wang, Anoop Cherian, and Stephen Gould. Human action forecasting by learning task grammars. arXiv:1709.06391, 2017. 3
[17] Tengda Han, Weidi Xie, and Andrew Zisserman. Temporal alignment networks for long-term video. In IEEE Conference on Computer Vision and Pattern Recognition, pages 2906-2916, June 2022. 4, 9, 15
[18] Haroon Idrees, Amir R. Zamir, Yu-Gang Jiang, Alexander Gorban, Ivan Laptev, Rahul Sukthankar, and Mubarak Shah. The thumos challenge on action recognition for videos "in the wild". Computer Vision and Image Understanding, 155:1-23, 2017. 8, 15
[19] Dohwan Ko, Joonmyung Choi, Juyeon Ko, Shinyeong Noh, Kyoung-Woon On, Eun-Sol Kim, and Hyunwoo J Kim. Video-text representation learning via differentiable weak temporal alignment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5016-5025, 2022. 9
[20] Mahnaz Koupae and William Yang Wang. Wikihow: A large scale text summarization dataset. ArXiv, abs/1810.09305, 2018. wikiHow dataset license available at: https://github.com/mahnazkoupae/WikiHow-Dataset.2,3
[21] Ranjay Krishna, Kenji Hata, Frederic Ren, Li Fei-Fei, and Juan Carlos Niebles. Dense-captioning events in videos. In International Conference on Computer Vision (ICCV), 2017. 3, 4, 8
[22] Hilde Kuehne, Ali Arslan, and Thomas Serre. The language of actions: Recovering the syntax and semantics of goal-directed human activities. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 780-787, 2014. 3
[23] Jie Lei, Tamara L. Berg, and Mohit Bansal. Qvhighlights: Detecting moments and highlights in videos via natural language queries. In Neural Information Processing Systems, 2021. 3, 4, 15
[24] Tsung-Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, C. Lawrence Zitnick, and Piotr Dólar. Microsoft coco: Common objects in context. European Conference on Computer Vision (ECCV), 2014. 8
[25] Xudong Lin, Fabio Petroni, Gedas Bertasius, Marcus Rohrbach, Shih-Fu Chang, and Lorenzo Torresani. Learning to recognize procedural activities with distant supervision. arXiv preprint arXiv:2201.10990, 2022. 2, 3, 7, 8, 13
[26] Ye Liu, Siyuan Li, Yang Wu, Chang Wen Chen, Ying Shan, and Xiaohu Qie. Umt: Unified multi-modal transformers for joint video moment retrieval and highlight detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 3042-3051, 2022. 3, 7, 8
[27] Huaishao Luo, Lei Ji, Botian Shi, Haoyang Huang, Nan Duan, Tianrui Li, Jason Li, Taroon Bharti, and Ming Zhou. Univl: A unified video and language pre-training model for multimodal understanding and generation. arXiv preprint arXiv:2002.06353, 2020.9
[28] Effrosyni Mavroudi, Triantafyllos Afouras, and Lorenzo Torresani. Learning to ground instructional articles in videos through narrations. In International Conference on Computer Vision (ICCV), 2023. 3, 5, 7, 8, 14, 15
[29] Effrosyni Mavroudi and René Vidal. Weakly-supervised generation and grounding of visual descriptions with conditional generative models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15544-15554, 2022. 3
[30] Antoine Miech, Jean-Baptiste Alayrac, Lucas Smaira, Ivan Laptev, Josef Sivic, and Andrew Zisserman. End-to-End Learning of Visual Representations from Uncurated Instructional Videos. In CVPR, 2020. 2, 7, 9, 14
[31] Antoine Miech, Dimitri Zhukov, Jean-Baptiste Alayrac, Ivan Laptev, and Josef Sivic. Howto100m: Learning a text-video embedding by watching hundred million narrated video clips. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2019. HT100M dataset license available at: https://github.com/antoine77340/howto100m/blob/master/License.2,9
[32] Tomas Mikolov, Kai Chen, Greg S. Corrado, and Jeffrey Dean. Efficient estimation of word representations in vector space. In International Conference on Learning Representations, 2013. 7
[33] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervision, 2021. 7
[34] Michaela Regneri, Marcus Rohrbach, Dominikus Wetzel, Stefan Thater, Bernt Schiele, and Manfred Pinkal. Grounding action descriptions in videos. Transactions of the Association for Computational Linguistics, 1:25-36, 2013. 3