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Cross-modal retrieval between videos and texts has gained increasing research interest due to the rapid emergence of videos on the web.
Collecting highly parallel data for paraphrase evaluation
David L. Chen and William B. Dolan · 2011
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Unifying visual-semantic embeddings with multimodal neural language models
Ryan Kiros, Ruslan Salakhutdinov, and Richard S. Zemel · 2014
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A dataset for movie description
Anna Rohrbach, Marcus Rohrbach, Niket Tandon, and Bernt Schiele · 2015
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Translating videos to natural language using deep recurrent neural networks
Subhashini Venugopalan, Huijuan Xu, Jeff Donahue, Marcus Rohrbach, Raymond J. Mooney, and Kate Saenko · 2015
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MSR-VTT: A large video description dataset for bridging video and language
Jun Xu, Tao Mei, Ting Yao, and Yong Rui · 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 C. Russell · 2017
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Movie description
Anna Rohrbach, Atousa Torabi, Marcus Rohrbach, Niket Tandon, Christopher Joseph Pal, Hugo Larochelle, Aaron C. Courville, and Bernt Schiele · 2017
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Informedia@ trecvid 2018: Ad-hoc video search with discrete and continuous representations
Po-Yao Huang, Junwei Liang, Vaibhav Vaibhav, Xiaojun Chang, and Alexander Hauptmann · 2018
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A joint sequence fusion model for video question answering and retrieval
Youngjae Yu, Jongseok Kim, and Gunhee Kim · 2018
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Learning a text-video embedding from incomplete and heterogeneous data
Antoine Miech, Ivan Laptev, and Josef Sivic · 2018
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Cross-modal and hierarchical modeling of video and text
Bowen Zhang, Hexiang Hu, and Fei Sha · 2018
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VSE++: improving visual-semantic embeddings with hard negatives
Fartash Faghri, David J. Fleet, Jamie Ryan Kiros, and Sanja Fidler · 2018
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Learning joint embedding with multimodal cues for cross-modal video-text retrieval
Niluthpol Chowdhury Mithun, Juncheng Li, Florian Metze, and Amit K. Roy-Chowdhury · 2018
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Use what you have: Video retrieval using representations from collaborative experts
Yang Liu, Samuel Albanie, Arsha Nagrani, and Andrew Zisserman · 2019
Cited alongside, same era.
HowTo100M: Learning a text-video embedding by watching hundred million narrated video clips
Antoine Miech, Dimitri Zhukov, Jean-Baptiste Alayrac, Makarand Tapaswi, Ivan Laptev, and Josef Sivic · 2019
Cited alongside, same era.
Mmvg-inf-etrol@ trecvid 2019: Activities in extended video
Xiaojun Chang, Wenhe Liu, Po-Yao Huang, Changlin Li, Fengda Zhu, Mingfei Han, Mingjie Li, Mengyuan Ma, Siyi Hu, Guoliang Kang, et al · 2019
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Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych · 2019
Cited alongside, same era.
ViLBERT: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks
Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee · 2019
Cited alongside, same era.
Dig into multi-modal cues for video retrieval with hierarchical alignment
Wenzhe Wang, Mengdan Zhang, Runnan Chen, Guanyu Cai, Penghao Zhou, Pai Peng, Xiaowei Guo, Jian Wu, and Xing Sun · 2021
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TACo: Token-aware cascade contrastive learning for video-text alignment
Jianwei Yang, Yonatan Bisk, and Jianfeng Gao · 2021
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On semantic similarity in video retrieval
Michael Wray, Hazel Doughty, and Dima Damen · 2021
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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, Gretchen Krueger, and Ilya Sutskever · 2021
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Is space-time attention all you need for video understanding?
Gedas Bertasius, Heng Wang, and Lorenzo Torresani · 2021
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VICReg: Variance-invariance-covariance regularization for self-supervised learning
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Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf · 2019
Cited alongside, same era.
Multi-modal transformer for video retrieval
Valentin Gabeur, Chen Sun, Karteek Alahari, and Cordelia Schmid · 2020
Cited alongside, same era.
ActBERT: Learning global-local video-text representations
Linchao Zhu and Yi Yang · 2020
Cited alongside, same era.
Fine-grained video-text retrieval with hierarchical graph reasoning
Shizhe Chen, Yida Zhao, Qin Jin, and Qi Wu · 2020
Cited alongside, same era.
Less is more: ClipBERT for video-and-language learning via sparse sampling
Jie Lei, Linjie Li, Luowei Zhou, Zhe Gan, Tamara L. Berg, Mohit Bansal, and Jingjing Liu · 2021
Cited alongside, same era.
Frozen in time: A joint video and image encoder for end-to-end retrieval
Max Bain, Arsha Nagrani, Gül Varol, and Andrew Zisserman · 2021
Cited alongside, same era.
Probabilistic embeddings for cross-modal retrieval
Sanghyuk Chun, Seong Joon Oh, Rafael Sampaio De Rezende, Yannis Kalantidis, and Diane Larlus · 2021
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Adrien Bardes, Jean Ponce, and Yann LeCun · 2021
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Noise estimation using density estimation for self-supervised multimodal learning
Elad Amrani, Rami Ben-Ari, Daniel Rotman, and Alex M. Bronstein · 2021
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AVLnet: Learning audio-visual language representations from instructional videos
Andrew Rouditchenko, Angie W. Boggust, David Harwath, Brian Chen, Dhiraj Joshi, Samuel Thomas, Kartik Audhkhasi, Hilde Kuehne, Rameswar Panda, Rogério Schmidt Feris, Brian Kingsbury, Michael Picheny, Antonio Torralba, and James R. Glass · 2021
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Teachtext: Crossmodal generalized distillation for text-video retrieval
Ioana Croitoru, Simion-Vlad Bogolin, Marius Leordeanu, Hailin Jin, Andrew Zisserman, Samuel Albanie, and Yang Liu · 2021
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Dual encoding for video retrieval by text
Jianfeng Dong, Xirong Li, Chaoxi Xu, Xun Yang, Gang Yang, Xun Wang, and Meng Wang · 2021
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Support-set bottlenecks for video-text representation learning
Mandela Patrick, Po-Yao Huang, Yuki Markus Asano, Florian Metze, Alexander G. Hauptmann, João F. Henriques, and Andrea Vedaldi · 2021
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Junnan Li, Dongxu Li, Caiming Xiong, and Steven C. H. Hoi · 2022
Closest in time.
Zeyu Wang, Yu Wu, Karthik Narasimhan, and Olga Russakovsky · 2022
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