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Recent advancements in multimodal large language models (MLLMs) have shown promising results, yet existing approaches struggle to effectively handle both temporal and spatial localization simultaneously.
Multimodal pretraining for dense video captioning
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Sahar Kazemzadeh, Vicente Ordonez, Mark Matten, and Tamara Berg · 2014
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I Loshchilov · 2017
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On the effectiveness of task granularity for transfer learning
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Luowei Zhou, Chenliang Xu, and Jason Corso · 2018
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Yansong Tang, Dajun Ding, Yongming Rao, Yu Zheng, Danyang Zhang, Lili Zhao, Jiwen Lu, and Jie Zhou · 2019
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Tianrui Hui, Si Liu, Shaofei Huang, Guanbin Li, Sansi Yu, Faxi Zhang, and Jizhong Han · 2020
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Seonguk Seo, Joon-Young Lee, and Bohyung Han · 2020
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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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Zhu Zhang, Zhou Zhao, Yang Zhao, Qi Wang, Huasheng Liu, and Lianli Gao · 2020
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Abhay Zala, Jaemin Cho, Satwik Kottur, Xilun Chen, Barlas Oguz, Yashar Mehdad, and Mohit Bansal · 2023
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Sigmoid loss for language image pre-training
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Antoine Yang, Antoine Miech, Josef Sivic, Ivan Laptev, and Cordelia Schmid · 2021
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Zihan Ding, Zi-han Ding, Tianrui Hui, Junshi Huang, Xiaoming Wei, Xiaolin Wei, and Si Liu · 2022
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Llama-adapter v2: Parameter-efficient visual instruction model
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Video-llama: An instruction-tuned audio-visual language model for video understanding
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Controllable navigation instruction generation with chain of thought prompting
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Longvlm: Efficient long video understanding via large language models
Yuetian Weng, Mingfei Han, Haoyu He, Xiaojun Chang, and Bohan Zhuang · 2025
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A large cross-modal video retrieval dataset with reading comprehension
Weijia Wu, Yuzhong Zhao, Zhuang Li, Jiahong Li, Hong Zhou, Mike Zheng Shou, and Xiang Bai · 2025
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