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Video-language pre-trained models have shown remarkable success in guiding video question-answering (VideoQA) tasks.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Fast r-cnn
Ross Girshick · 2015
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Exploring models and data for image question answering
Mengye Ren, Ryan Kiros, and Richard Zemel · 2015
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Movieqa: Understanding stories in movies through question-answering
Makarand Tapaswi, Yukun Zhu, Rainer Stiefelhagen, Antonio Torralba, Raquel Urtasun, and Sanja Fidler · 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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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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Deepstory: Video story qa by deep embedded memory networks
Kyung-Min Kim, Min-Oh Heo, Seong-Ho Choi, and Byoung-Tak Zhang · 2017
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Marioqa: Answering questions by watching gameplay videos
Jonghwan Mun, Paul Hongsuck Seo, Ilchae Jung, and Bohyung Han · 2017
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Video question answering via attribute-augmented attention network learning
Yunan Ye, Zhou Zhao, Yimeng Li, Long Chen, Jun Xiao, and Yueting Zhuang · 2017
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Motion-appearance co-memory networks for video question answering
Jiyang Gao, Runzhou Ge, Kan Chen, and Ram Nevatia · 2018
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Tvqa: Localized, compositional video question answering
Jie Lei, Licheng Yu, Mohit Bansal, and Tamara L Berg · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
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Explore multi-step reasoning in video question answering
Xiaomeng Song, Yucheng Shi, Xin Chen, and Yahong Han · 2018
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A better way to attend: Attention with trees for video question answering
Hongyang Xue, Wenqing Chu, Zhou Zhao, and Deng Cai · 2018
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Tutorialvqa: Question answering dataset for tutorial videos
Anthony Colas, Seokhwan Kim, Franck Dernoncourt, Siddhesh Gupte, Daisy Zhe Wang, and Doo Soon Kim · 2019
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Heterogeneous memory enhanced multimodal attention model for video question answering
Chenyou Fan, Xiaofan Zhang, Shu Zhang, Wensheng Wang, Chi Zhang, and Heng Huang · 2019
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Tvqa+: Spatio-temporal grounding for video question answering
Jie Lei, Licheng Yu, Tamara L Berg, and Mohit Bansal · 2019
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Visualbert: A simple and performant baseline for vision and language
Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, and Kai-Wei Chang · 2019
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Beyond rnns: Positional self-attention with co-attention for video question answering
Xiangpeng Li, Jingkuan Song, Lianli Gao, Xianglong Liu, Wenbing Huang, Xiangnan He, and Chuang Gan · 2019
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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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Revisiting fine-tuning for few-shot learning
Akihiro Nakamura and Tatsuya Harada · 2019
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To tune or not to tune? adapting pretrained representations to diverse tasks
Matthew E Peters, Sebastian Ruder, and Noah A Smith · 2019
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Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander H Miller, and Sebastian Riedel · 2019
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Bert is not a knowledge base (yet): Factual knowledge vs. name-based reasoning in unsupervised qa
Nina Poerner, Ulli Waltinger, and Hinrich Schütze · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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Videobert: A joint model for video and language representation learning
Chen Sun, Austin Myers, Carl Vondrick, Kevin Murphy, and Cordelia Schmid · 2019
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Spatiotemporal-textual co-attention network for video question answering
Zheng-Jun Zha, Jiawei Liu, Tianhao Yang, and Yongdong Zhang · 2019
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Lifeqa: A real-life dataset for video question answering
Santiago Castro, Mahmoud Azab, Jonathan Stroud, Cristina Noujaim, Ruoyao Wang, Jia Deng, and Rada Mihalcea · 2020
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Aman Chadha, Gurneet Arora, and Navpreet Kaloty · 2020
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Knowit vqa: Answering knowledge-based questions about videos
Noa Garcia, Mayu Otani, Chenhui Chu, and Yuta Nakashima · 2020
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Location-aware graph convolutional networks for video question answering
Deng Huang, Peihao Chen, Runhao Zeng, Qing Du, Mingkui Tan, and Chuang Gan · 2020
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Divide and conquer: Question-guided spatio-temporal contextual attention for video question answering
Jianwen Jiang, Ziqiang Chen, Haojie Lin, Xibin Zhao, and Yue Gao · 2020
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Reasoning with heterogeneous graph alignment for video question answering
Pin Jiang and Yahong Han · 2020
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How can we know what language models know?
Zhengbao Jiang, Frank F Xu, Jun Araki, and Graham Neubig · 2020
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Dense-caption matching and frame-selection gating for temporal localization in videoqa
Hyounghun Kim, Zineng Tang, and Mohit Bansal · 2020
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Modality shifting attention network for multi-modal video question answering
Junyeong Kim, Minuk Ma, Trung Pham, Kyungsu Kim, and Chang D Yoo · 2020
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Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang · 2021
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Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen · 2021
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Bridge to answer: Structure-aware graph interaction network for video question answering
Jungin Park, Jiyoung Lee, and Kwanghoon Sohn · 2021
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Styleclip: Text-driven manipulation of stylegan imagery
Or Patashnik, Zongze Wu, Eli Shechtman, Daniel Cohen-Or, and Dani Lischinski · 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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Hierarchical conditional relation networks for video question answering
Thao Minh Le, Vuong Le, Svetha Venkatesh, and Truyen Tran · 2020
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Hero: Hierarchical encoder for video+ language omni-representation pre-training
Linjie Li, Yen-Chun Chen, Yu Cheng, Zhe Gan, Licheng Yu, and Jingjing Liu · 2020
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Univl: A unified video and language pre-training model for multimodal understanding and generation
Huaishao Luo, Lei Ji, Botian Shi, Haoyang Huang, Nan Duan, Tianrui Li, Jason Li, Taroon Bharti, and Ming Zhou · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Autoprompt: Eliciting knowledge from language models with automatically generated prompts
Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, and Sameer Singh · 2020
Cited alongside, same era.
Bert representations for video question answering
Zekun Yang, Noa Garcia, Chenhui Chu, Mayu Otani, Yuta Nakashima, and Haruo Takemura · 2020
Cited alongside, same era.
Actbert: Learning global-local video-text representations
Linchao Zhu and Yi Yang · 2020
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Denseclip: Language-guided dense prediction with context-aware prompting
Yongming Rao, Wenliang Zhao, Guangyi Chen, Yansong Tang, Zheng Zhu, Guan Huang, Jie Zhou, and Jiwen Lu · 2021
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Video question answering with phrases via semantic roles
Arka Sadhu, Kan Chen, and Ram Nevatia · 2021
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Look before you speak: Visually contextualized utterances
Paul Hongsuck Seo, Arsha Nagrani, and Cordelia Schmid · 2021
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Multimodal few-shot learning with frozen language models
Maria Tsimpoukelli, Jacob L Menick, Serkan Cabi, SM Eslami, Oriol Vinyals, and Felix Hill · 2021
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Actionclip: A new paradigm for video action recognition
Mengmeng Wang, Jiazheng Xing, and Yong Liu · 2021
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Vlmo: Unified vision-language pre-training with mixture-of-modality-experts
Wenhui Wang, Hangbo Bao, Li Dong, and Furu Wei · 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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Vlm: Task-agnostic video-language model pre-training for video understanding
Hu Xu, Gargi Ghosh, Po-Yao Huang, Prahal Arora, Masoumeh Aminzadeh, Christoph Feichtenhofer, Florian Metze, and Luke Zettlemoyer · 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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Sutd-trafficqa: A question answering benchmark and an efficient network for video reasoning over traffic events
Li Xu, He Huang, and Jun Liu · 2021
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Just ask: Learning to answer questions from millions of narrated videos
Antoine Yang, Antoine Miech, Josef Sivic, Ivan Laptev, and Cordelia Schmid · 2021
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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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Vt-clip: Enhancing vision-language models with visual-guided texts
Renrui Zhang, Longtian Qiu, Wei Zhang, and Ziyao Zeng · 2021
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Denseclip: Extract free dense labels from clip
Chong Zhou, Chen Change Loy, and Bo Dai · 2021
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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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Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge Belongie, Bharath Hariharan, and Ser-Nam Lim · 2022
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Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi · 2022
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Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
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Motionclip: Exposing human motion generation to clip space
Guy Tevet, Brian Gordon, Amir Hertz, Amit H Bermano, and Daniel Cohen-Or · 2022
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Zero-shot video question answering via frozen bidirectional language models
Antoine Yang, Antoine Miech, Josef Sivic, Ivan Laptev, and Cordelia Schmid · 2022
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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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Tip-adapter: Training-free clip-adapter for better vision-language modeling
Renrui Zhang, Rongyao Fang, Peng Gao, Wei Zhang, Kunchang Li, Jifeng Dai, Yu Qiao, and Hongsheng Li · 2022
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Conditional prompt learning for vision-language models
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu · 2022
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Learning to prompt for vision-language models
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu · 2022
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Plot: Prompt learning with optimal transport for vision-language models
Guangyi Chen, Weiran Yao, Xiangchen Song, Xinyue Li, Yongming Rao, and Kun Zhang · 2023
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