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We present HourVideo, a benchmark dataset for hour-long video-language understanding.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael S. Bernstein, Alexander C. Berg, and Li Fei-Fei · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick · 2014
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Vqa: Visual question answering
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh · 2015
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The kinetics human action video dataset
Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, et al · 2017
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The "something something" video database for learning and evaluating visual common sense
Raghav Goyal, Samira Ebrahimi Kahou, Vincent Michalski, Joanna Materzynska, Susanne Westphal, Heuna Kim, Valentin Haenel, Ingo Fründ, Peter Yianilos, Moritz Mueller-Freitag, Florian Hoppe, Christian Thurau, Ingo Bax, and Roland Memisevic · 2017
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Video question answering via gradually refined attention over appearance and motion
Dejing Xu, Zhou Zhao, Jun Xiao, Fei Wu, Hanwang Zhang, Xiangnan He, and Yueting Zhuang · 2017
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TGIF-QA: toward spatio-temporal reasoning in visual question answering
Yunseok Jang, Yale Song, Youngjae Yu, Youngjin Kim, and Gunhee Kim · 2017
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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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TVQA: Localized, compositional video question answering
Jie Lei, Licheng Yu, Mohit Bansal, and Tamara Berg · 2018
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Towards VQA models that can read
Amanpreet Singh, Vivek Natarajan, Meet Shah, Yu Jiang, Xinlei Chen, Dhruv Batra, Devi Parikh, and Marcus Rohrbach · 2019
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GQA: A new dataset for real-world visual reasoning and compositional question answering
Drew A. Hudson and Christopher D. Manning · 2019
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Activitynet-qa: A dataset for understanding complex web videos via question answering
Zhou Yu, Dejing Xu, Jun Yu, Ting Yu, Zhou Zhao, Yueting Zhuang, and Dacheng Tao · 2019
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman · 2019
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A large-scale study of representation learning with the visual task adaptation benchmark
Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov, Pierre Ruyssen, Carlos Riquelme, Mario Lucic, Josip Djolonga, Andre Susano Pinto, Maxim Neumann, Alexey Dosovitskiy, et al · 2019
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Long-term feature banks for detailed video understanding
Chao-Yuan Wu, Christoph Feichtenhofer, Haoqi Fan, Kaiming He, Philipp Krahenbuhl, and Ross Girshick · 2019
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Video object segmentation using space-time memory networks
Seoung Wug Oh, Joon-Young Lee, Ning Xu, and Seon Joo Kim · 2019
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Movienet: A holistic dataset for movie understanding
Qingqiu Huang, Yu Xiong, Anyi Rao, Jiaze Wang, and Dahua Lin · 2020
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Multibench: Multiscale benchmarks for multimodal representation learning
Paul Pu Liang, Yiwei Lyu, Xiang Fan, Zetian Wu, Yun Cheng, Jason Wu, Leslie Chen, Peter Wu, Michelle A Lee, Yuke Zhu, et al · 2021
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Value: A multi-task benchmark for video-and-language understanding evaluation
Linjie Li, Jie Lei, Zhe Gan, Licheng Yu, Yen-Chun Chen, Rohit Pillai, Yu Cheng, Luowei Zhou, Xin Eric Wang, William Yang Wang, et al · 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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Next-qa: Next phase of question-answering to explaining temporal actions
Junbin Xiao, Xindi Shang, Angela Yao, and Tat-Seng Chua · 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, et al · 2021
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Scaling up visual and vision-language representation learning with noisy text supervision
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig · 2021
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A large-scale study on unsupervised spatiotemporal representation learning
Christoph Feichtenhofer, Haoqi Fan, Bo Xiong, Ross Girshick, and Kaiming He · 2021
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Enhancing self-supervised video representation learning via multi-level feature optimization
Rui Qian, Yuxi Li, Huabin Liu, John See, Shuangrui Ding, Xian Liu, Dian Li, and Weiyao Lin · 2021
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Flamingo: a visual language model for few-shot learning
Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, et al · 2022
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Ego4d: Around the world in 3,000 hours of egocentric video
Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, et al · 2022
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Robustness analysis of video-language models against visual and language perturbations
Madeline C Schiappa, Shruti Vyas, Hamid Palangi, Yogesh S Rawat, and Vibhav Vineet · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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BLIP: bootstrapping language-image pre-training for unified vision-language understanding and generation
Junnan Li, Dongxu Li, Caiming Xiong, and Steven C. H. Hoi · 2022
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Dual contrastive learning for spatio-temporal representation
Shuangrui Ding, Rui Qian, and Hongkai Xiong · 2022
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Motion-inductive self-supervised object discovery in videos
Shuangrui Ding, Weidi Xie, Yabo Chen, Rui Qian, Xiaopeng Zhang, Hongkai Xiong, and Qi Tian · 2022
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Static and dynamic concepts for self-supervised video representation learning
Rui Qian, Shuangrui Ding, Xian Liu, and Dahua Lin · 2022
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Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training
Zhan Tong, Yibing Song, Jue Wang, and Limin Wang · 2022
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Revisiting the" video" in video-language understanding
Shyamal Buch, Cristóbal Eyzaguirre, Adrien Gaidon, Jiajun Wu, Li Fei-Fei, and Juan Carlos Niebles · 2022
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Memvit: Memory-augmented multiscale vision transformer for efficient long-term video recognition
Chao-Yuan Wu, Yanghao Li, Karttikeya Mangalam, Haoqi Fan, Bo Xiong, Jitendra Malik, and Christoph Feichtenhofer · 2022
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Xmem: Long-term video object segmentation with an atkinson-shiffrin memory model
Ho Kei Cheng and Alexander G Schwing · 2022
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GPT-4V(ision) system card, 2023
OpenAI · 2023
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Socratic models: Composing zero-shot multimodal reasoning with language
Andy Zeng, Maria Attarian, brian ichter, Krzysztof Marcin Choromanski, Adrian Wong, Stefan Welker, Federico Tombari, Aveek Purohit, Michael S Ryoo, Vikas Sindhwani, Johnny Lee, Vincent Vanhoucke, and Pete Florence · 2023
Cited alongside, same era.
Moviechat: From dense token to sparse memory for long video understanding
Enxin Song, Wenhao Chai, Guanhong Wang, Yucheng Zhang, Haoyang Zhou, Feiyang Wu, Xun Guo, Tianbo Ye, Yang Lu, Jenq-Neng Hwang, and Gaoang Wang · 2023
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Video-teller: Enhancing cross-modal generation with fusion and decoupling
Haogeng Liu, Qihang Fan, Tingkai Liu, Linjie Yang, Yunzhe Tao, Huaibo Huang, Ran He, and Hongxia Yang · 2023
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Video-chatgpt: Towards detailed video understanding via large vision and language models
Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan · 2023
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Chat-univi: Unified visual representation empowers large language models with image and video understanding
Peng Jin, Ryuichi Takanobu, Caiwan Zhang, Xiaochun Cao, and Li Yuan · 2023
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Memory-and-anticipation transformer for online action understanding
Jiahao Wang, Guo Chen, Yifei Huang, Limin Wang, and Tong Lu · 2023
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alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
Cited alongside, same era.
Mvbench: A comprehensive multi-modal video understanding benchmark
Kunchang Li, Yali Wang, Yinan He, Yizhuo Li, Yi Wang, Yi Liu, Zun Wang, Jilan Xu, Guo Chen, Ping Luo, et al · 2023
Cited alongside, same era.
Autoeval-video: An automatic benchmark for assessing large vision language models in open-ended video question answering
Xiuyuan Chen, Yuan Lin, Yuchen Zhang, and Weiran Huang · 2023
Cited alongside, same era.
Are current long-term video understanding datasets long-term?
Ombretta Strafforello, Klamer Schutte, and Jan van Gemert · 2023
Cited alongside, same era.
Internvid: A large-scale video-text dataset for multimodal understanding and generation
Yi Wang, Yinan He, Yizhuo Li, Kunchang Li, Jiashuo Yu, Xin Ma, Xinhao Li, Guo Chen, Xinyuan Chen, Yaohui Wang, et al · 2023
Cited alongside, same era.
Can i trust your answer? visually grounded video question answering
Junbin Xiao, Yao Angela, Yicong Li, and Tat-Seng Chua · 2023
Cited alongside, same era.
Moviechat: From dense token to sparse memory for long video understanding
Enxin Song, Wenhao Chai, Guanhong Wang, Yucheng Zhang, Haoyang Zhou, Feiyang Wu, Xun Guo, Tian Ye, Yan Lu, Jenq-Neng Hwang, et al · 2023
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Valley: Video assistant with large language model enhanced ability
Ruipu Luo, Ziwang Zhao, Min Yang, Junwei Dong, Minghui Qiu, Pengcheng Lu, Tao Wang, and Zhongyu Wei · 2023
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Video-llava: Learning united visual representation by alignment before projection
Bin Lin, Bin Zhu, Yang Ye, Munan Ning, Peng Jin, and Li Yuan · 2023
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Peng Jin, Ryuichi Takanobu, Caiwan Zhang, Xiaochun Cao, and Li Yuan · 2023
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A simple llm framework for long-range video question-answering
Ce Zhang, Taixi Lu, Md Mohaiminul Islam, Ziyang Wang, Shoubin Yu, Mohit Bansal, and Gedas Bertasius · 2023
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Timechat: A time-sensitive multimodal large language model for long video understanding
Shuhuai Ren, Linli Yao, Shicheng Li, Xu Sun, and Lu Hou · 2023
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Vtimellm: Empower llm to grasp video moments
Bin Huang, Xin Wang, Hong Chen, Zihan Song, and Wenwu Zhu · 2023
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Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Machel Reid, Nikolay Savinov, Denis Teplyashin, Dmitry Lepikhin, Timothy Lillicrap, Jean-baptiste Alayrac, Radu Soricut, Angeliki Lazaridou, Orhan Firat, Julian Schrittwieser, et al · 2024
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Egoschema: A diagnostic benchmark for very long-form video language understanding
Karttikeya Mangalam, Raiymbek Akshulakov, and Jitendra Malik · 2024
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Llava-next: A strong zero-shot video understanding model, April 2024
Yuanhan Zhang, Bo Li, haotian Liu, Yong jae Lee, Liangke Gui, Di Fu, Jiashi Feng, Ziwei Liu, and Chunyuan Li · 2024
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Star: A benchmark for situated reasoning in real-world videos
Bo Wu, Shoubin Yu, Zhenfang Chen, Joshua B Tenenbaum, and Chuang Gan · 2024
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Tempcompass: Do video llms really understand videos?
Yuanxin Liu, Shicheng Li, Yi Liu, Yuxiang Wang, Shuhuai Ren, Lei Li, Sishuo Chen, Xu Sun, and Lu Hou · 2024
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Panda-70m: Captioning 70m videos with multiple cross-modality teachers
Tsai-Shien Chen, Aliaksandr Siarohin, Willi Menapace, Ekaterina Deyneka, Hsiang-wei Chao, Byung Eun Jeon, Yuwei Fang, Hsin-Ying Lee, Jian Ren, Ming-Hsuan Yang, et al · 2024
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Video-chatgpt: Towards detailed video understanding via large vision and language models
Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan · 2024
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Perception test: A diagnostic benchmark for multimodal video models
Viorica Patraucean, Lucas Smaira, Ankush Gupta, Adria Recasens, Larisa Markeeva, Dylan Banarse, Skanda Koppula, Mateusz Malinowski, Yi Yang, Carl Doersch, et al · 2024
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Mathverse: Does your multi-modal llm truly see the diagrams in visual math problems?
Renrui Zhang, Dongzhi Jiang, Yichi Zhang, Haokun Lin, Ziyu Guo, Pengshuo Qiu, Aojun Zhou, Pan Lu, Kai-Wei Chang, Peng Gao, et al · 2024
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Openeqa: Embodied question answering in the era of foundation models
Arjun Majumdar, Anurag Ajay, Xiaohan Zhang, Pranav Putta, Sriram Yenamandra, Mikael Henaff, Sneha Silwal, Paul Mcvay, Oleksandr Maksymets, Sergio Arnaud, Karmesh Yadav, Qiyang Li, Ben Newman, Mohit Sharma, Vincent Berges, Shiqi Zhang, Pulkit Agrawal, Yonatan Bisk, Dhruv Batra, Mrinal Kalakrishnan, Franziska Meier, Chris Paxton, Sasha Sax, and Aravind Rajeswaran · 2024
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Multimodal arxiv: A dataset for improving scientific comprehension of large vision-language models
Lei Li, Yuqi Wang, Runxin Xu, Peiyi Wang, Xiachong Feng, Lingpeng Kong, and Qi Liu · 2024
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Visual instruction tuning
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee · 2024
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Internlm-xcomposer2: Mastering free-form text-image composition and comprehension in vision-language large model
Xiaoyi Dong, Pan Zhang, Yuhang Zang, Yuhang Cao, Bin Wang, Linke Ouyang, Xilin Wei, Songyang Zhang, Haodong Duan, Maosong Cao, et al · 2024
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Pegasus-v1 technical report
Raehyuk Jung, Hyojun Go, Jaehyuk Yi, Jiho Jang, Daniel Kim, Jay Suh, Aiden SJ Lee, Cooper Han, Jae Lee, Jeff Kim, Jin-Young Kim, Junwan Kim, Kyle Park, Lucas Lee, Mars Ha, Minjoon Seo, Abraham Jo, Ed Park, Hassan Kianinejad, SJ Kim, Tony Moon, Wade Jeong, Andrei Popescu, Esther Kim, EK Yoon, Genie Heo, Henry Choi, Jenna Kang, Kevin Han, Noah Seo, Sunny Nguyen, Ryan Won, Ye Eun Park, Anthony Giuliani, Dave Chung, Hans Yoon, James Le, Jenny Ahn, June Lee, Maninder Saini, Meredith Sanders, Soyoung Lee, Sue Kim, and Travis Couture · 2024
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Pllava : Parameter-free llava extension from images to videos for video dense captioning
Lin Xu, Yilin Zhao, Daquan Zhou, Zhijie Lin, See Kiong Ng, and Jiashi Feng · 2024
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Memory consolidation enables long-context video understanding
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Ma-lmm: Memory-augmented large multimodal model for long-term video understanding
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Longvlm: Efficient long video understanding via large language models
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Tarsier: Recipes for training and evaluating large video description models
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