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Multimodal Large Language Models (MLLMs) have recently demonstrated strong performance across a wide range of vision-language tasks, garnering significant attention in the computer vision.
Some methods for classification and analysis of multivariate observations
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Least squares quantization in pcm
Stuart Lloyd · 1982
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Pearson correlation coefficient
Israel Cohen, Yiteng Huang, Jingdong Chen, Jacob Benesty, Jacob Benesty, Jingdong Chen, Yiteng Huang, and Israel Cohen · 2009
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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
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Spearman’s rank correlation coefficient
Philip Sedgwick · 2014
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard · 2017
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Show, observe and tell: Attribute-driven attention model for image captioning
Hui Chen, Guiguang Ding, Zijia Lin, Sicheng Zhao, and Jungong Han · 2018
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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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Towards vqa models that can read
Amanpreet Singh, Vivek Natarajan, Meet Shah, Yu Jiang, Xinlei Chen, Dhruv Batra, Devi Parikh, and Marcus Rohrbach · 2019
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Imram: Iterative matching with recurrent attention memory for cross-modal image-text retrieval
Hui Chen, Guiguang Ding, Xudong Liu, Zijia Lin, Ji Liu, and Jungong Han · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy · 2020
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Designing network design spaces
Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, and Piotr Dollár · 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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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
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Token merging: Your vit but faster
Daniel Bolya, Cheng-Yang Fu, Xiaoliang Dai, Peizhao Zhang, Christoph Feichtenhofer, and Judy Hoffman · 2022
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Not all patches are what you need: Expediting vision transformers via token reorganizations
Youwei Liang, Chongjian Ge, Zhan Tong, Yibing Song, Jue Wang, and Pengtao Xie · 2022
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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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Introducing our multimodal models, 2023
Rohan Bavishi, Erich Elsen, Curtis Hawthorne, Maxwell Nye, Augustus Odena, Arushi Somani, and Sağnak Taşırlar · 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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Mobilevlm: A fast, reproducible and strong vision language assistant for mobile devices
Xiangxiang Chu, Limeng Qiao, Xinyang Lin, Shuang Xu, Yang Yang, Yiming Hu, Fei Wei, Xinyu Zhang, Bo Zhang, Xiaolin Wei, et al · 2023
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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 Fung, and Steven Hoi · 2023
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A survey on multimodal large language models for autonomous driving
Can Cui, Yunsheng Ma, Xu Cao, Wenqian Ye, Yang Zhou, Kaizhao Liang, Jintai Chen, Juanwu Lu, Zichong Yang, Kuei-Da Liao, et al · 2024
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Chatglm: A family of large language models from glm-130b to glm-4 all tools
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Anomalygpt: Detecting industrial anomalies using large vision-language models
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Danny Driess, Fei Xia, Mehdi SM Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, et al · 2023
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Introducing idefics: An open reproduction of state-of-the-art visual language model
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Phi-2: The surprising power of small language models
Mojan Javaheripi, Sébastien Bubeck, Marah Abdin, Jyoti Aneja, Sebastien Bubeck, Caio César Teodoro Mendes, Weizhu Chen, Allie Del Giorno, Ronen Eldan, Sivakanth Gopi, et al · 2023
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Mmbench: Is your multi-modal model an all-around player?
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The impact of multimodal large language models on health care’s future
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Gemini: a family of highly capable multimodal models
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Teaching matters: Investigating the role of supervision in vision transformers
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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
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Muyang He, Yexin Liu, Boya Wu, Jianhao Yuan, Yueze Wang, Tiejun Huang, and Bo Zhao · 2024
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Moe-llava: Mixture of experts for large vision-language models
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Robollm: Robotic vision tasks grounded on multimodal large language models
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Llarva: Vision-action instruction tuning enhances robot learning
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Llava-prumerge: Adaptive token reduction for efficient large multimodal models
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A comprehensive survey of large language models and multimodal large language models in medicine
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Drivegpt4: Interpretable end-to-end autonomous driving via large language model
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Deco: Decoupling token compression from semantic abstraction in multimodal large language models
Linli Yao, Lei Li, Shuhuai Ren, Lean Wang, Yuanxin Liu, Xu Sun, and Lu Hou · 2024
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Voco-llama: Towards vision compression with large language models
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Object recognition as next token prediction
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Llava-phi: Efficient multi-modal assistant with small language model, 2024
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