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This paper introduces InternVid, a large-scale video-centric multimodal dataset that enables learning powerful and transferable video-text representations for multimodal understanding and generation.
Collecting highly parallel data for paraphrase evaluation
David L Chen and William B Dolan · 2011
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Ucf101: A dataset of 101 human actions classes from videos in the wild
Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah · 2012
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Activitynet: A large-scale video benchmark for human activity understanding
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A dataset for movie description
Anna Rohrbach, Marcus Rohrbach, Niket Tandon, and Bernt Schiele · 2015
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Yfcc100m: The new data in multimedia research
Bart Thomee, David A Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li · 2016
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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 Russell · 2017
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Movie description
Anna Rohrbach, Atousa Torabi, Marcus Rohrbach, Niket Tandon, Christopher Pal, Hugo Larochelle, Aaron Courville, and Bernt Schiele · 2017
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Dense-captioning events in videos
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Quo vadis, action recognition? a new model and the kinetics dataset
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The" something something" video database for learning and evaluating visual common sense
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Tracking by natural language specification
Zhenyang Li, Ran Tao, Efstratios Gavves, Cees G. M. Snoek, and Arnold W. M. Smeulders · 2017
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Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning
Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut · 2018
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Towards automatic learning of procedures from web instructional videos
Luowei Zhou, Chenliang Xu, and Jason Corso · 2018
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How2: a large-scale dataset for multimodal language understanding
Ramon Sanabria, Ozan Caglayan, Shruti Palaskar, Desmond Elliott, Loïc Barrault, Lucia Specia, and Florian Metze · 2018
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On the effectiveness of task granularity for transfer learning
Farzaneh Mahdisoltani, Guillaume Berger, Waseem Gharbieh, David Fleet, and Roland Memisevic · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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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
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Videobert: A joint model for video and language representation learning
Chen Sun, Austin Myers, Carl Vondrick, Kevin P. Murphy, and Cordelia Schmid · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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End-to-end learning of visual representations from uncurated instructional videos
Antoine Miech, Jean-Baptiste Alayrac, Lucas Smaira, Ivan Laptev, Josef Sivic, and Andrew Zisserman · 2020
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Learning spatiotemporal features via video and text pair discrimination
Tianhao Li and Limin Wang · 2020
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Actbert: Learning global-local video-text representations
Linchao Zhu and Yi Yang · 2020
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Learning video representations from textual web supervision
Jonathan C Stroud, Zhichao Lu, Chen Sun, Jia Deng, Rahul Sukthankar, Cordelia Schmid, and David A Ross · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2020
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Merlot: Multimodal neural script knowledge models
Rowan Zellers, Ximing Lu, Jack Hessel, Youngjae Yu, Jae Sung Park, Jize Cao, Ali Farhadi, and Yejin Choi · 2021
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Frozen in time: A joint video and image encoder for end-to-end retrieval
Max Bain, Arsha Nagrani, Gül Varol, and Andrew Zisserman · 2021
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Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts
Soravit Changpinyo, Piyush Sharma, Nan Ding, and Radu Soricut · 2021
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Redcaps: Web-curated image-text data created by the people, for the people
Karan Desai, Gaurav Kaul, Zubin Aysola, and Justin Johnson · 2021
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Wit: Wikipedia-based image text dataset for multimodal multilingual machine learning
Krishna Srinivasan, Karthik Raman, Jiecao Chen, Michael Bendersky, and Marc Najork · 2021
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Videoclip: Contrastive pre-training for zero-shot video-text understanding
Hu Xu, Gargi Ghosh, Po-Yao Huang, Dmytro Okhonko, Armen Aghajanyan, Florian Metze, Luke Zettlemoyer, and Christoph Feichtenhofer · 2021
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How much can clip benefit vision-and-language tasks?
Sheng Shen, Liunian Harold Li, Hao Tan, Mohit Bansal, Anna Rohrbach, Kai-Wei Chang, Zhewei Yao, and Kurt Keutzer · 2021
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation
Jay Zhangjie Wu, Yixiao Ge, Xintao Wang, Weixian Lei, Yuchao Gu, Wynne Hsu, Ying Shan, Xiaohu Qie, and Mike Zheng Shou · 2022
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Long video generation with time-agnostic vqgan and time-sensitive transformer
Songwei Ge, Thomas Hayes, Harry Yang, Xi Yin, Guan Pang, David Jacobs, Jia-Bin Huang, and Devi Parikh · 2022
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Openflamingo, 2023
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Minigpt-4: Enhancing vision-language understanding with advanced large language models
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny · 2023
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Violet: End-to-end video-language transformers with masked visual-token modeling
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Co-grounding networks with semantic attention for referring expression comprehension in videos
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Learning transferable visual models from natural language supervision
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Advancing high-resolution video-language representation with large-scale video transcriptions
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Merlot reserve: Neural script knowledge through vision and language and sound
Rowan Zellers, Jiasen Lu, Ximing Lu, Youngjae Yu, Yanpeng Zhao, Mohammadreza Salehi, Aditya Kusupati, Jack Hessel, Ali Farhadi, and Yejin Choi · 2022
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Flamingo: a visual language model for few-shot learning
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Videochat: Chat-centric video understanding
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Internchat: Solving vision-centric tasks by interacting with chatbots beyond language
Zhaoyang Liu, Yinan He, Wenhai Wang, Weiyun Wang, Yi Wang, Shoufa Chen, Qinglong Zhang, Yang Yang, Qingyun Li, Jiashuo Yu, et al · 2023
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Wanjuan: A comprehensive multimodal dataset for advancing english and chinese large models
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Unmasked teacher: Towards training-efficient video foundation models
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Learning transferable spatiotemporal representations from natural script knowledge
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Tvtsv2: Learning out-of-the-box spatiotemporal visual representations at scale
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Videollm: Modeling video sequence with large language models
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Videomae v2: Scaling video masked autoencoders with dual masking
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mplug-2: A modularized multi-modal foundation model across text, image and video
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Vlab: Enhancing video language pre-training by feature adapting and blending
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Tag2text: Guiding vision-language model via image tagging
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Language is not all you need: Aligning perception with language models
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Datacomp: In search of the next generation of multimodal datasets
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Eva-clip: Improved training techniques for clip at scale
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Align your latents: High-resolution video synthesis with latent diffusion models
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Video-chatgpt: Towards detailed video understanding via large vision and language models
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Mimic-it: Multi-modal in-context instruction tuning
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