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Large Multimodal Models (LMMs) have emerged as powerful models capable of understanding various data modalities, including text, images, and videos.
Verb semantics and lexical selection
Zhibiao Wu and Martha Palmer · 1994
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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Grasp and path relinking for the max–min diversity problem
Mauricio GC Resende, Rafael Martí, Micael Gallego, and Abraham Duarte · 2010
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A simple and effective algorithm for the maxmin diversity problem
Daniel Cosmin Porumbel, Jin-Kao Hao, and Fred Glover · 2011
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Microsoft coco: Common objects in context, 2015
Tsung-Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, C. Lawrence Zitnick, and Piotr Dollár · 2015
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Cider: Consensus-based image description evaluation
Ramakrishna Vedantam, C. Lawrence Zitnick, and Devi Parikh · 2015
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Making the V in VQA matter: Elevating the role of image understanding in Visual Question Answering
Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh · 2017
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Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models
Bryan A. Plummer, Liwei Wang, Christopher M. Cervantes, Juan C. Caicedo, Julia Hockenmaier, and Svetlana Lazebnik · 2017
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Vizwiz grand challenge: Answering visual questions from blind people
Danna Gurari, Qing Li, Abigale J Stangl, Anhong Guo, Chi Lin, Kristen Grauman, Jiebo Luo, and Jeffrey P Bigham · 2018
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nocaps: novel object captioning at scale
Harsh Agrawal, Karan Desai, Yufei Wang, Xinlei Chen, Rishabh Jain, Mark Johnson, Dhruv Batra, Devi Parikh, Stefan Lee, and Peter Anderson · 2019
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Gqa: A new dataset for real-world visual reasoning and compositional question answering
Drew A Hudson and Christopher D Manning · 2019
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Ok-vqa: A visual question answering benchmark requiring external knowledge
Kenneth Marino, Mohammad Rastegari, Ali Farhadi, and Roozbeh Mottaghi · 2019
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Towards vqa models that can read
Amanpreet Singh, Vivek Natarjan, Meet Shah, Yu Jiang, Xinlei Chen, Devi Parikh, and Marcus Rohrbach · 2019
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Adaptive attention span in transformers
Sainbayar Sukhbaatar, Edouard Grave, Piotr Bojanowski, and Armand Joulin · 2019
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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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Rethinking attention with performers
Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Davis, Afroz Mohiuddin, Lukasz Kaiser, et al · 2020
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Transformers are rnns: Fast autoregressive transformers with linear attention
Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and François Fleuret · 2020
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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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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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Flashattention: Fast and memory-efficient exact attention with io-awareness
Tri Dao, Dan Fu, Stefano Ermon, Atri Rudra, and Christopher Ré · 2022
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Learn to explain: Multimodal reasoning via thought chains for science question answering, 2022
Pan Lu, Swaroop Mishra, Tony Xia, Liang Qiu, Kai-Wei Chang, Song-Chun Zhu, Oyvind Tafjord, Peter Clark, and Ashwin Kalyan · 2022
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A review on discrete diversity and dispersion maximization from an or perspective
Rafael Martí, Anna Martínez-Gavara, Sergio Pérez-Peló, and Jesús Sánchez-Oro · 2022
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Tokenpacker: Efficient visual projector for multimodal llm
Wentong Li, Yuqian Yuan, Jian Liu, Dongqi Tang, Song Wang, Jianke Zhu, and Lei Zhang · 2024
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Boosting multimodal large language models with visual tokens withdrawal for rapid inference
Zhihang Lin, Mingbao Lin, Luxi Lin, and Rongrong Ji · 2024
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Llava-next: Improved reasoning, ocr, and world knowledge, 2024
Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, and Yong Jae Lee · 2024
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Video-chatgpt: Towards detailed video understanding via large vision and language models
Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan · 2024
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Hello gpt-4o, 2024
OpenAI · 2024
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E Gonzalez, et al · 2023
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Mamba: Linear-time sequence modeling with selective state spaces
Albert Gu and Tri Dao · 2023
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Video-llava: Learning united visual representation by alignment before projection
Bin Lin, Yang Ye, Bin Zhu, Jiaxi Cui, Munan Ning, Peng Jin, and Li Yuan · 2023
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Visual instruction tuning
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee · 2023
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Egoschema: A diagnostic benchmark for very long-form video language understanding, 2023
Karttikeya Mangalam, Raiymbek Akshulakov, and Jitendra Malik · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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Yanyuan Qiao, Zheng Yu, Longteng Guo, Sihan Chen, Zijia Zhao, Mingzhen Sun, Qi Wu, and Jing Liu · 2024
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Llava-prumerge: Adaptive token reduction for efficient large multimodal models
Yuzhang Shang, Mu Cai, Bingxin Xu, Yong Jae Lee, and Yan Yan · 2024
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Skipping computations in multimodal llms
Mustafa Shukor and Matthieu Cord · 2024
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Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Gemini Team, Petko Georgiev, Ving Ian Lei, Ryan Burnell, Libin Bai, Anmol Gulati, Garrett Tanzer, Damien Vincent, Zhufeng Pan, Shibo Wang, et al · 2024
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Llavadi: What matters for multimodal large language models distillation
Shilin Xu, Xiangtai Li, Haobo Yuan, Lu Qi, Yunhai Tong, and Ming-Hsuan Yang · 2024
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Minicpm-v: A gpt-4v level mllm on your phone
Yuan Yao, Tianyu Yu, Ao Zhang, Chongyi Wang, Junbo Cui, Hongji Zhu, Tianchi Cai, Haoyu Li, Weilin Zhao, Zhihui He, et al · 2024
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Fit and prune: Fast and training-free visual token pruning for multi-modal large language models
Weihao Ye, Qiong Wu, Wenhao Lin, and Yiyi Zhou · 2024
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Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi
Xiang Yue, Yuansheng Ni, Kai Zhang, Tianyu Zheng, Ruoqi Liu, Ge Zhang, Samuel Stevens, Dongfu Jiang, Weiming Ren, Yuxuan Sun, Cong Wei, Botao Yu, Ruibin Yuan, Renliang Sun, Ming Yin, Boyuan Zheng, Zhenzhu Yang, Yibo Liu, Wenhao Huang, Huan Sun, Yu Su, and Wenhu Chen · 2024
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Tinyllava: A framework of small-scale large multimodal models
Baichuan Zhou, Ying Hu, Xi Weng, Junlong Jia, Jie Luo, Xien Liu, Ji Wu, and Lei Huang · 2024
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Matryoshka multimodal models
Mu Cai, Jianwei Yang, Jianfeng Gao, and Yong Jae Lee · 2025
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Mmbench: Is your multi-modal model an all-around player?
Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, et al · 2025
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