Fetching the paper…
Reading the bibliography…
Video understanding is a crucial next step for multimodal large language models (MLLMs).
Collecting highly parallel data for paraphrase evaluation
David Chen and William B Dolan · 2011
Earlier work this paper cites.
Activitynet: A large-scale video benchmark for human activity understanding
Fabian Caba Heilbron, Victor Escorcia, Bernard Ghanem, and Juan Carlos Niebles · 2015
Earlier work this paper cites.
Msr-vtt: A large video description dataset for bridging video and language
Jun Xu, Tao Mei, Ting Yao, and Yong Rui · 2016
Earlier work this paper cites.
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
Earlier work this paper cites.
Tgif-qa: Toward spatio-temporal reasoning in visual question answering
Yunseok Jang, Yale Song, Youngjae Yu, Youngjin Kim, and Gunhee Kim · 2017
Earlier work this paper cites.
ivqa: Inverse visual question answering
Feng Liu, Tao Xiang, Timothy M Hospedales, Wankou Yang, and Changyin Sun · 2018
Earlier work this paper cites.
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
Earlier work this paper cites.
Agqa: A benchmark for compositional spatio-temporal reasoning
Madeleine Grunde-McLaughlin, Ranjay Krishna, and Maneesh Agrawala · 2021
Earlier work this paper cites.
Next-qa: Next phase of question-answering to explaining temporal actions
Junbin Xiao, Xindi Shang, Angela Yao, and Tat-Seng Chua · 2021
Earlier work this paper cites.
Xiuyuan Chen, Yuan Lin, Yuchen Zhang, and Weiran Huang · 2023
Earlier work this paper cites.
Needle in a haystack–pressure testing llms, 2023
G Kamradt · 2023
Earlier work this paper cites.
How long can context length of open-source llms truly promise?
Dacheng Li, Rulin Shao, Anze Xie, Ying Sheng, Lianmin Zheng, Joseph Gonzalez, Ion Stoica, Xuezhe Ma, and Hao Zhang · 2023
Earlier work this paper cites.
Video-llava: Learning united visual representation by alignment before projection
Bin Lin, Bin Zhu, Yang Ye, Munan Ning, Peng Jin, and Li Yuan · 2023
Earlier work this paper cites.
Llava-plus: Learning to use tools for creating multimodal agents
Shilong Liu, Hao Cheng, Haotian Liu, Hao Zhang, Feng Li, Tianhe Ren, Xueyan Zou, Jianwei Yang, Hang Su, Jun Zhu, et al · 2023
Earlier work this paper cites.
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
Cited alongside, same era.
Video-chatgpt: Towards detailed video understanding via large vision and language models
Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan · 2023
Cited alongside, same era.
Landmark attention: Random-access infinite context length for transformers
Amirkeivan Mohtashami and Martin Jaggi · 2023
Cited alongside, same era.
Video-bench: A comprehensive benchmark and toolkit for evaluating video-based large language models
Munan Ning, Bin Zhu, Yujia Xie, Bin Lin, Jiaxi Cui, Lu Yuan, Dongdong Chen, and Li Yuan · 2023
Cited alongside, same era.
Gpt-4 technical report, 2023
Ruler: What’s the real context size of your long-context language models?
Cheng-Ping Hsieh, Simeng Sun, Samuel Kriman, Shantanu Acharya, Dima Rekesh, Fei Jia, and Boris Ginsburg · 2024
Closest in time.
Llava-onevision: Easy visual task transfer
Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li, Ziwei Liu, and Chunyuan Li · 2024
Closest in time.
Llava-next: Improved reasoning, ocr, and world knowledge, January 2024b
Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, and Yong Jae Lee · 2024
Closest in time.
Deepseek-vl: towards real-world vision-language understanding
Haoyu Lu, Wen Liu, Bo Zhang, Bingxuan Wang, Kai Dong, Bo Liu, Jingxiang Sun, Tongzheng Ren, Zhuoshu Li, Yaofeng Sun, et al · 2024
Closest in time.
Egoschema: A diagnostic benchmark for very long-form video language understanding
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
OpenAI · 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
Cited alongside, same era.
Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al · 2023
Cited alongside, same era.
Cogvlm: Visual expert for pretrained language models
Weihan Wang, Qingsong Lv, Wenmeng Yu, Wenyi Hong, Ji Qi, Yan Wang, Junhui Ji, Zhuoyi Yang, Lei Zhao, Xixuan Song, et al · 2023
Cited alongside, same era.
Sigmoid loss for language image pre-training
Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer · 2023
Cited alongside, same era.
Video-llama: An instruction-tuned audio-visual language model for video understanding
Hang Zhang, Xin Li, and Lidong Bing · 2023
Cited alongside, same era.
Chatbridge: Bridging modalities with large language model as a language catalyst
Zijia Zhao, Longteng Guo, Tongtian Yue, Sihan Chen, Shuai Shao, Xinxin Zhu, Zehuan Yuan, and Jing Liu · 2023
Cited alongside, same era.
Towards event-oriented long video understanding
Yifan Du, Kun Zhou, Yuqi Huo, Yifan Li, Wayne Xin Zhao, Haoyu Lu, Zijia Zhao, Bingning Wang, Weipeng Chen, and Ji-Rong Wen · 2024
Cited alongside, same era.
Karttikeya Mangalam, Raiymbek Akshulakov, and Jitendra Malik · 2024
Closest in time.
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
Closest in time.
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
Closest in time.
Mingyang Song, Mao Zheng, and Xuan Luo · 2024
Closest in time.
Unveiling selection biases: Exploring order and token sensitivity in large language models
Sheng-Lun Wei, Cheng-Kuang Wu, Hen-Hsen Huang, and Hsin-Hsi Chen · 2024
Closest in time.
Longvideobench: A benchmark for long-context interleaved video-language understanding
Haoning Wu, Dongxu Li, Bei Chen, and Junnan Li · 2024
Closest in time.
An Yang, Baosong Yang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Zhou, Chengpeng Li, Chengyuan Li, Dayiheng Liu, Fei Huang, et al · 2024
Closest in time.
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
Closest in time.
Mlvu: A comprehensive benchmark for multi-task long video understanding
Junjie Zhou, Yan Shu, Bo Zhao, Boya Wu, Shitao Xiao, Xi Yang, Yongping Xiong, Bo Zhang, Tiejun Huang, and Zheng Liu · 2024
Closest in time.