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Large-scale multi-modal training with image-text pairs imparts strong generalization to CLIP model.
Hmdb: a large video database for human motion recognition
Hildegard Kuehne, Hueihan Jhuang, Estíbaliz Garrote, Tomaso Poggio, and Thomas Serre · 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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Evaluation of output embeddings for fine-grained image classification
Zeynep Akata, Scott Reed, Daniel Walter, Honglak Lee, and Bernt Schiele · 2015
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An embarrassingly simple approach to zero-shot learning
Bernardino Romera-Paredes and Philip Torr · 2015
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Spatiotemporal residual networks for video action recognition
R Christoph and Feichtenhofer Axel Pinz · 2016
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Temporal segment networks: Towards good practices for deep action recognition
Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaoou Tang, and Luc Van Gool · 2016
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Quo vadis, action recognition? a new model and the kinetics dataset
Joao Carreira and Andrew Zisserman · 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 Fruend, Peter Yianilos, Moritz Mueller-Freitag, et al · 2017
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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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Zero-shot action recognition with error-correcting output codes
Jie Qin, Li Liu, Ling Shao, Fumin Shen, Bingbing Ni, Jiaxin Chen, and Yunhong Wang · 2017
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Alternative semantic representations for zero-shot human action recognition
Qian Wang and Ke Chen · 2017
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Spatiotemporal pyramid network for video action recognition
Yunbo Wang, Mingsheng Long, Jianmin Wang, and Philip S Yu · 2017
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Learning a deep embedding model for zero-shot learning
Li Zhang, Tao Xiang, and Shaogang Gong · 2017
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A short note about kinetics-600
Joao Carreira, Eric Noland, Andras Banki-Horvath, Chloe Hillier, and Andrew Zisserman · 2018
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Towards universal representation for unseen action recognition
Yi Zhu, Yang Long, Yu Guan, Shawn Newsam, and Ling Shao · 2018
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Slowfast networks for video recognition
Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, and Kaiming He · 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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ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee · 2019
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Rethinking zero-shot video classification: End-to-end training for realistic applications
Biagio Brattoli, Joseph Tighe, Fedor Zhdanov, Pietro Perona, and Krzysztof Chalupka · 2020
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UNITER: UNiversal Image-TExt Representation Learning
Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu · 2020
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All about knowledge graphs for actions
Pallabi Ghosh, Nirat Saini, Larry S Davis, and Abhinav Shrivastava · 2020
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Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks
Xiujun Li, Xi Yin, Chunyuan Li, Pengchuan Zhang, Xiaowei Hu, Lei Zhang, Lijuan Wang, Houdong Hu, Li Dong, Furu Wei, et al · 2020
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Prompting visual-language models for efficient video understanding
Chen Ju, Tengda Han, Kunhao Zheng, Ya Zhang, and Weidi Xie · 2022
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Maple: Multi-modal prompt learning
Muhammad Uzair khattak, Hanoona Rasheed, Muhammad Maaz, Salman Khan, and Fahad Shahbaz Khan · 2022
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Language-driven semantic segmentation
Boyi Li, Kilian Q Weinberger, Serge Belongie, Vladlen Koltun, and Rene Ranftl · 2022
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Uniformer: Unifying convolution and self-attention for visual recognition
Kunchang Li, Yali Wang, Junhao Zhang, Peng Gao, Guanglu Song, Yu Liu, Hongsheng Li, and Yu Qiao · 2022
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Video swin transformer
Ze Liu, Jia Ning, Yue Cao, Yixuan Wei, Zheng Zhang, Stephen Lin, and Han Hu · 2022
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Video swin transformer
Ze Liu, Jia Ning, Yue Cao, Yixuan Wei, Zheng Zhang, Stephen Lin, and Han Hu · 2022
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Is space-time attention all you need for video understanding?
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Elaborative rehearsal for zero-shot action recognition
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Scaling up visual and vision-language representation learning with noisy text supervision
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MDETR–Modulated Detection for End-to-End Multi-Modal Understanding
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Learning transferable visual models from natural language supervision
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Denseclip: Extract free dense labels from clip
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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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Detecting twenty-thousand classes using image-level supervision
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