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Multimodal and large language models (LLMs) have revolutionized the utilization of open-world knowledge, unlocking novel potentials across various tasks and applications.
Multi-task deep visual-semantic embedding for video thumbnail selection
Wu Liu, Tao Mei, Yongdong Zhang, Cherry Che, and Jiebo Luo · 2015
Earlier work this paper cites.
To click or not to click: Automatic selection of beautiful thumbnails from videos
Yale Song, Miriam Redi, Jordi Vallmitjana, and Alejandro Jaimes · 2016
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Localizing moments in video with natural language
Lisa Anne Hendricks, Oliver Wang, Eli Shechtman, Josef Sivic, Trevor Darrell, and Bryan Russell · 2017
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Temporal localization of moments in video collections with natural language
Victor Escorcia, Mattia Soldan, Josef Sivic, Bernard Ghanem, and Bryan Russell · 2019
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Slowfast networks for video recognition
Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, and Kaiming He · 2019
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End-to-end object detection with transformers
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020
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Tvr: A large-scale dataset for video-subtitle moment retrieval
Jie Lei, Licheng Yu, Tamara L Berg, and Mohit Bansal · 2020
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Detecting moments and highlights in videos via natural language queries
Jie Lei, Tamara L Berg, and Mohit Bansal · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Cited alongside, same era.
Plug-and-play vqa: Zero-shot vqa by conjoining large pretrained models with zero training
Anthony Meng Huat Tiong, Junnan Li, Boyang Li, Silvio Savarese, and Steven CH Hoi · 2022
Cited alongside, same era.
Zero-shot composed image retrieval with textual inversion
Alberto Baldrati, Lorenzo Agnolucci, Marco Bertini, and Alberto Del Bimbo · 2023
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Parameter-efficient multi-modal highlight detection via prompting
Donghoon Han, Seong-Uk Nam, Eunhwan Park, and Nojun Kwak · 2023
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Knowing where to focus: Event-aware transformer for video grounding
Jinhyun Jang, Jungin Park, Jin Kim, Hyeongjun Kwon, and Kwanghoon Sohn · 2023
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Decap: Decoding CLIP latents for zero-shot captioning via text-only training
Wei Li, Linchao Zhu, Longyin Wen, and Yi Yang · 2023
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Univtg: Towards unified video-language temporal grounding
Kevin Qinghong Lin, Pengchuan Zhang, Joya Chen, Shraman Pramanick, Difei Gao, Alex Jinpeng Wang, Rui Yan, and Mike Zheng Shou
Cited in the paper.
Univtg: Towards unified video-language temporal grounding
Kevin Qinghong Lin, Pengchuan Zhang, Joya Chen, Shraman Pramanick, Difei Gao, Alex Jinpeng Wang, Rui Yan, and Mike Zheng Shou
Cited in the paper.
Umt: Unified multi-modal transformers for joint video moment retrieval and highlight detection
Ye Liu, Siyuan Li, Yang Wu, Chang-Wen Chen, Ying Shan, and Xiaohu Qie
Cited in the paper.
Umt: Unified multi-modal transformers for joint video moment retrieval and highlight detection
Ye Liu, Siyuan Li, Yang Wu, Chang-Wen Chen, Ying Shan, and Xiaohu Qie
Cited in the paper.
WonJun Moon, Sangeek Hyun, SuBeen Lee, and Jae-Pil Heo
Cited in the paper.
Query-dependent video representation for moment retrieval and highlight detection
WonJun Moon, Sangeek Hyun, SangUk Park, Dongchan Park, and Jae-Pil Heo
Cited in the paper.
Conditional prompt learning for vision-language models
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu
Cited in the paper.