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With the rapid advancement of multimodal large language models (MLLMs), their evaluation has become increasingly comprehensive.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
Earlier work this paper cites.
Towards ai-complete question answering: A set of prerequisite toy tasks
Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M Rush, Bart Van Merriënboer, Armand Joulin, and Tomas Mikolov · 2015
Earlier work this paper cites.
Movieqa: Understanding stories in movies through question-answering
Makarand Tapaswi, Yukun Zhu, Rainer Stiefelhagen, Antonio Torralba, Raquel Urtasun, and Sanja Fidler · 2016
Earlier work this paper cites.
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
Earlier work this paper cites.
Visual genome: Connecting language and vision using crowdsourced dense image annotations
Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A Shamma, et al · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Gqa: A new dataset for real-world visual reasoning and compositional question answering
Drew A Hudson and Christopher D Manning · 2019
Earlier work this paper cites.
Ok-vqa: A visual question answering benchmark requiring external knowledge
Kenneth Marino, Mohammad Rastegari, Ali Farhadi, and Roozbeh Mottaghi · 2019
Earlier work this paper cites.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Earlier work this paper cites.
Movienet: A holistic dataset for movie understanding
Qingqiu Huang, Yu Xiong, Anyi Rao, Jiaze Wang, and Dahua Lin · 2020
Earlier work this paper cites.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush · 2020
Earlier work this paper cites.
Docvqa: A dataset for vqa on document images
Minesh Mathew, Dimosthenis Karatzas, and CV Jawahar · 2021
Earlier work this paper cites.
Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation
Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi · 2022
Earlier work this paper cites.
Flamingo: a visual language model for few-shot learning
Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, et al · 2022
Earlier work this paper cites.
A-okvqa: A benchmark for visual question answering using world knowledge
Dustin Schwenk, Apoorv Khandelwal, Christopher Clark, Kenneth Marino, and Roozbeh Mottaghi · 2022
Earlier work this paper cites.
Learn to explain: Multimodal reasoning via thought chains for science question answering
Pan Lu, Swaroop Mishra, Tanglin Xia, Liang Qiu, Kai-Wei Chang, Song-Chun Zhu, Oyvind Tafjord, Peter Clark, and Ashwin Kalyan · 2022
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Vila: Improving structured content extraction from scientific pdfs using visual layout groups
Zejiang Shen, Kyle Lo, Lucy Lu Wang, Bailey Kuehl, Daniel S Weld, and Doug Downey · 2022
Earlier work this paper cites.
Flashattention: Fast and memory-efficient exact attention with io-awareness
Tri Dao, Dan Fu, Stefano Ermon, Atri Rudra, and Christopher Ré · 2022
Earlier work this paper cites.
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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Visionllm: Large language model is also an open-ended decoder for vision-centric tasks
Wenhai Wang, Zhe Chen, Xiaokang Chen, Jiannan Wu, Xizhou Zhu, Gang Zeng, Ping Luo, Tong Lu, Jie Zhou, Yu Qiao, et al · 2023
Earlier work this paper cites.
Generative multimodal models are in-context learners
Quan Sun, Yufeng Cui, Xiaosong Zhang, Fan Zhang, Qiying Yu, Zhengxiong Luo, Yueze Wang, Yongming Rao, Jingjing Liu, Tiejun Huang, et al · 2023
Earlier work this paper cites.
Vl-gpt: A generative pre-trained transformer for vision and language understanding and generation
Jinguo Zhu, Xiaohan Ding, Yixiao Ge, Yuying Ge, Sijie Zhao, Hengshuang Zhao, Xiaohua Wang, and Ying Shan · 2023
Cited alongside, same era.
Gpt-4v(ision) system card
OpenAI · 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.
Gpt4roi: Instruction tuning large language model on region-of-interest
Shilong Zhang, Peize Sun, Shoufa Chen, Min Xiao, Wenqi Shao, Wenwei Zhang, Kai Chen, and Ping Luo · 2023
Cited alongside, same era.
Blink: Multimodal large language models can see but not perceive
Xingyu Fu, Yushi Hu, Bangzheng Li, Yu Feng, Haoyu Wang, Xudong Lin, Dan Roth, Noah A Smith, Wei-Chiu Ma, and Ranjay Krishna · 2024
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The claude 3 model family: Opus, sonnet, haiku
Anthropic · 2024
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Introducing hpt: A family of leading multimodal llms
HyperGAI Research Team · 2024
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Mm1: Methods, analysis & insights from multimodal llm pre-training
Brandon McKinzie, Zhe Gan, Jean-Philippe Fauconnier, Sam Dodge, Bowen Zhang, Philipp Dufter, Dhruti Shah, Xianzhi Du, Futang Peng, Floris Weers, et al · 2024
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Step-1v: A hundred billion parameter multimodal large model
StepFun Research Team · 2024
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Shengqiong Wu, Hao Fei, Leigang Qu, Wei Ji, and Tat-Seng Chua · 2023
Cited alongside, same era.
Lisa: Reasoning segmentation via large language model
Xin Lai, Zhuotao Tian, Yukang Chen, Yanwei Li, Yuhui Yuan, Shu Liu, and Jiaya Jia · 2023
Cited alongside, same era.
Kosmos-2: Grounding multimodal large language models to the world
Zhiliang Peng, Wenhui Wang, Li Dong, Yaru Hao, Shaohan Huang, Shuming Ma, and Furu Wei · 2023
Cited alongside, same era.
Mme: A comprehensive evaluation benchmark for multimodal large language models
Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Zhenyu Qiu, Wei Lin, Jinrui Yang, Xiawu Zheng, et al · 2023
Cited alongside, same era.
Mm-vet: Evaluating large multimodal models for integrated capabilities
Weihao Yu, Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Zicheng Liu, Xinchao Wang, and Lijuan Wang · 2023
Cited alongside, same era.
Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts
Pan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu, Chunyuan Li, Hannaneh Hajishirzi, Hao Cheng, Kai-Wei Chang, Michel Galley, and Jianfeng Gao · 2023
Cited alongside, same era.
mplug-owl: Modularization empowers large language models with multimodality
Qinghao Ye, Haiyang Xu, Guohai Xu, Jiabo Ye, Ming Yan, Yiyang Zhou, Junyang Wang, Anwen Hu, Pengcheng Shi, Yaya Shi, et al · 2023
Cited alongside, same era.
Needle In A Haystack - pressure testing LLMs
Gregory Kamradt · 2023
Cited alongside, same era.
X.ai · 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
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Moe-llava: Mixture of experts for large vision-language models
Bin Lin, Zhenyu Tang, Yang Ye, Jiaxi Cui, Bin Zhu, Peng Jin, Junwu Zhang, Munan Ning, and Li Yuan · 2024
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Instructblip: Towards general-purpose vision-language models with instruction tuning
Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale N Fung, and Steven Hoi · 2024
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Minigpt-4: Enhancing vision-language understanding with advanced large language models
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny · 2024
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Jiannan Wu, Muyan Zhong, Sen Xing, Zeqiang Lai, Zhaoyang Liu, Wenhai Wang, Zhe Chen, Xizhou Zhu, Lewei Lu, Tong Lu, et al · 2024
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Seed-bench-2-plus: Benchmarking multimodal large language models with text-rich visual comprehension
Bohao Li, Yuying Ge, Yi Chen, Yixiao Ge, Ruimao Zhang, and Ying Shan · 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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A survey on benchmarks of multimodal large language models
Jian Li and Weiheng Lu · 2024
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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
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Vbench: Comprehensive benchmark suite for video generative models
Ziqi Huang, Yinan He, Jiashuo Yu, Fan Zhang, Chenyang Si, Yuming Jiang, Yuanhan Zhang, Tianxing Wu, Qingyang Jin, Nattapol Chanpaisit, et al · 2024
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In search of needles in a 10m haystack: Recurrent memory finds what llms miss
Yuri Kuratov, Aydar Bulatov, Petr Anokhin, Dmitry Sorokin, Artyom Sorokin, and Mikhail Burtsev · 2024
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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
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FlashAttention-2: Faster attention with better parallelism and work partitioning
Tri Dao · 2024
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Yi: Open foundation models by 01. ai
Alex Young, Bei Chen, Chao Li, Chengen Huang, Ge Zhang, Guanwei Zhang, Heng Li, Jiangcheng Zhu, Jianqun Chen, Jing Chang, et al · 2024
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