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This paper tackles the problem of video question answering (VideoQA), a task that often requires multi-step reasoning and a profound understanding of spatial-temporal dynamics.
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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CLEVRER: collision events for video representation and reasoning
Kexin Yi, Chuang Gan, Yunzhu Li, Pushmeet Kohli, Jiajun Wu, Antonio Torralba, and Joshua B. Tenenbaum · 2020
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Madeleine Grunde-McLaughlin, Ranjay Krishna, and Maneesh Agrawala · 2021
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Learning transferable visual models from natural language supervision
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Star: A benchmark for situated reasoning in real-world videos
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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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Flamingo: a visual language model for few-shot learning
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Ego4d: Around the world in 3,000 hours of egocentric video
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Grounded language-image pre-training
Liunian Harold Li*, Pengchuan Zhang*, Haotian Zhang*, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, Kai-Wei Chang, and Jianfeng Gao · 2022
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Simple open-vocabulary object detection with vision transformers
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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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Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, Binyuan Hui, Luo Ji, Mei Li, Junyang Lin, Runji Lin, Dayiheng Liu, Gao Liu, Chengqiang Lu, Keming Lu, Jianxin Ma, Rui Men, Xingzhang Ren, Xuancheng Ren, Chuanqi Tan, Sinan Tan, Jianhong Tu, Peng Wang, Shijie Wang, Wei Wang, Shengguang Wu, Benfeng Xu, Jin Xu, An Yang, Hao Yang, Jian Yang, Shusheng Yang, Yang Yao, Bowen Yu, Hongyi Yuan, Zheng Yuan, Jianwei Zhang, Xingxuan Zhang, Yichang Zhang, Zhenru Zhang, Chang Zhou, Jingren Zhou, Xiaohuan Zhou, and Tianhang Zhu · 2023
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Zero-shot video question answering with procedural programs
Rohan Choudhury, Koichiro Niinuma, Kris M. Kitani, and Laszlo A. Jeni · 2023
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Visual programming: Compositional visual reasoning without training
Tanmay Gupta and Aniruddha Kembhavi · 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 · 2023
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Perception test: A diagnostic benchmark for multimodal video models
Viorica Pătrăucean, Lucas Smaira, Ankush Gupta, Adrià Recasens Continente, Larisa Markeeva, Dylan Banarse, Skanda Koppula, Joseph Heyward, Mateusz Malinowski, Yi Yang, Carl Doersch, Tatiana Matejovicova, Yury Sulsky, Antoine Miech, Alex Frechette, Hanna Klimczak, Raphael Koster, Junlin Zhang, Stephanie Winkler, Yusuf Aytar, Simon Osindero, Dima Damen, Andrew Zisserman, and João Carreira · 2023
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Vipergpt: Visual inference via python execution for reasoning
Dídac Surís, Sachit Menon, and Carl Vondrick · 2023
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Open-vclip: Transforming clip to an open-vocabulary video model via interpolated weight optimization
Video-llava: Learning united visual representation by alignment before projection
Bin Lin, Bin Zhu, Yang Ye, Munan Ning, Peng Jin, and Li Yuan · 2024
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Video-chatgpt: Towards detailed video understanding via large vision and language models
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Vurf: A general-purpose reasoning and self-refinement framework for video understanding
Ahmad Mahmood, Ashmal Vayani, Muzammal Naseer, Salman Khan, and Fahad Khan · 2024
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Morevqa: Exploring modular reasoning models for video question answering
Juhong Min, Shyamal Buch, Arsha Nagrani, Minsu Cho, and Cordelia Schmid · 2024
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Scaling open-vocabulary object detection
Matthias Minderer, Alexey Gritsenko, and Neil Houlsby · 2024
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Zejia Weng, Xitong Yang, Ang Li, Zuxuan Wu, and Yu-Gang Jiang · 2023
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Sigmoid loss for language image pre-training
Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer · 2023
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Llava-onevision: Easy visual task transfer
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Videollama 2: Advancing spatial-temporal modeling and audio understanding in video-llms
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Videoagent: A memory-augmented multimodal agent for video understanding
Yue Fan, Xiaojian Ma, Rujie Wu, Yuntao Du, Jiaqi Li, Zhi Gao, and Qing Li · 2024
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Video-of-thought: Step-by-step video reasoning from perception to cognition
Hao Fei, Shengqiong Wu, Wei Ji, Hanwang Zhang, Meishan Zhang, Mong-Li Lee, and Wynne Hsu · 2024
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Video-mme: The first-ever comprehensive evaluation benchmark of multi-modal llms in video analysis
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Visual cot: Advancing multi-modal language models with a comprehensive dataset and benchmark for chain-of-thought reasoning
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Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi
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Llava-next: A strong zero-shot video understanding model
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Video-star: Self-training enables video instruction tuning with any supervision
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