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In Multimodal Large Language Models (MLLMs), a visual projector plays a crucial role in bridging pre-trained vision encoders with LLMs, enabling profound visual understanding while harnessing the LLMs' robust capabilities.
Convolutional networks for images, speech, and time series
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Referitgame: Referring to Objects in Photographs of Natural Scenes
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Very deep convolutional networks for large-scale image recognition
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Generation and Comprehension of Unambiguous Object Descriptions
Junhua Mao, Jonathan Huang, Alexander Toshev, Oana Camburu, Alan L Yuille, and Kevin Murphy · 2016
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Modeling Context in Referring Expressions
Licheng Yu, Patrick Poirson, Shan Yang, Alexander C Berg, and Tamara L Berg · 2016
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Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering
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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
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Aggregated residual transformations for deep neural networks
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Squeeze-and-excitation networks
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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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OCR-VQA: Visual Question Answering by Reading Text in Images
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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, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Uniter: Universal Image-Text Representation Learning
Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu · 2020
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Zero: Memory optimizations toward training trillion parameter models
Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, and Yuxiong He · 2020
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VALUE: A Multi-Task Benchmark for Video-and-Language Understanding Evaluation
Linjie Li, Jie Lei, Zhe Gan, Licheng Yu, Yen-Chun Chen, Rohit Pillai, Yu Cheng, Luowei Zhou, Xin Eric Wang, William Yang Wang, et al · 2021
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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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Florence: A New Foundation Model for Computer Vision
Lu Yuan, Dongdong Chen, Yi-Ling Chen, Noel Codella, Xiyang Dai, Jianfeng Gao, Houdong Hu, Xuedong Huang, Boxin Li, Chunyuan Li, et al · 2021
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Deformable DETR: Deformable Transformers for End-to-End Object Detection
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Flamingo: a Visual Language Model for Few-Shot Learning
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COYO-700M: Image-Text Pair Dataset
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LoRA: Low-rank adaptation of large language models
Edward J Hu, yelong shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2022
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, Ke Li, Xing Sun, and Rongrong Ji · 2023
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LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model
Peng Gao, Jiaming Han, Renrui Zhang, Ziyi Lin, Shijie Geng, Aojun Zhou, Wei Zhang, Pan Lu, Conghui He, Xiangyu Yue, Hongsheng Li, and Yu Qiao · 2023
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3D-LLM: Injecting the 3D World into Large Language Models
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Large Language Models are Temporal and Causal Reasoners for Video Question Answering
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The flan collection: Designing data and methods for effective instruction tuning
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BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
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A convnet for the 2020s
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Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering
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A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V Le · 2022
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Coca: Contrastive captioners are image-text foundation models
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Can Large Language Models Be an Alternative to Human Evaluations?
Cheng-Han Chiang and Hung-yi Lee · 2023
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An Empirical Study of Scaling Instruct-tuned Large Multimodal Models
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Kosmos-2: Grounding Multimodal Large Language Models to the World
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PointLLM: Empowering Large Language Models to Understand Point Clouds
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Pink: Unveiling the Power of Referential Comprehension for Multi-modal LLMs
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mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality
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Ferret: Refer and Ground Anything Anywhere at Any Granularity
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MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities
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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, et al · 2023
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What Matters in Training a GPT4-Style Language Model with Multimodal Inputs?
Yan Zeng, Hanbo Zhang, Jiani Zheng, Jiangnan Xia, Guoqiang Wei, Yang Wei, Yuchen Zhang, and Tao Kong · 2023
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SVIT: Scaling up Visual Instruction Tuning
Bo Zhao, Boya Wu, and Tiejun Huang · 2023
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MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
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