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Multimodal Large Language Models (MLLMs) have achieved remarkable success in vision understanding, reasoning, and interaction.
Perplexity—a measure of the difficulty of speech recognition tasks
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Meteor: An automatic metric for mt evaluation with improved correlation with human judgments
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Estimating or propagating gradients through stochastic neurons for conditional computation
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Microsoft coco: Common objects in context
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
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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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Attention is all you need
A Vaswani · 2017
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Vizwiz grand challenge: Answering visual questions from blind people
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Convolutional networks with adaptive inference graphs
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Gqa: A new dataset for real-world visual reasoning and compositional question answering
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Pytorch: An imperative style, high-performance deep learning library
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Language models are unsupervised multitask learners
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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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Language models are few-shot learners
Tom B Brown · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy · 2020
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Channel selection using gumbel softmax
Charles Herrmann, Richard Strong Bowen, and Ramin Zabih · 2020
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Dynamicvit: Efficient vision transformers with dynamic token sparsification
Yongming Rao, Wenliang Zhao, Benlin Liu, Jiwen Lu, Jie Zhou, and Cho-Jui Hsieh · 2021
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
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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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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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Judging llm-as-a-judge with mt-bench and chatbot arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al · 2023
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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 · 2023
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Kazi Hasan Ibn Arif, JinYi Yoon, Dimitrios S Nikolopoulos, Hans Vandierendonck, Deepu John, and Bo Ji · 2024
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Honeybee: Locality-enhanced projector for multimodal llm
Junbum Cha, Wooyoung Kang, Jonghwan Mun, and Byungseok Roh · 2024
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Mobilevlm v2: Faster and stronger baseline for vision language model
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Qwen-vl: A frontier large vision-language model with versatile abilities
Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou · 2023
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Model tells you what to discard: Adaptive kv cache compression for llms
Suyu Ge, Yunan Zhang, Liyuan Liu, Minjia Zhang, Jiawei Han, and Jianfeng Gao · 2023
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Lm-infinite: Simple on-the-fly length generalization for large language models
Chi Han, Qifan Wang, Wenhan Xiong, Yu Chen, Heng Ji, and Sinong Wang · 2023
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Fast inference from transformers via speculative decoding
Yaniv Leviathan, Matan Kalman, and Yossi Matias · 2023
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Filter pruning for efficient cnns via knowledge-driven differential filter sampler
Shaohui Lin, Wenxuan Huang, Jiao Xie, Baochang Zhang, Yunhang Shen, Zhou Yu, Jungong Han, and David Doermann · 2023
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Internlm: A multilingual language model with progressively enhanced capabilities, 2023
InternLM Team · 2023
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To see is to believe: Prompting gpt-4v for better visual instruction tuning
Junke Wang, Lingchen Meng, Zejia Weng, Bo He, Zuxuan Wu, and Yu-Gang Jiang · 2023
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Xiangxiang Chu, Limeng Qiao, Xinyu Zhang, Shuang Xu, Fei Wei, Yang Yang, Xiaofei Sun, Yiming Hu, Xinyang Lin, Bo Zhang, et al · 2024
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Albert Q Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, et al · 2024
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Efficient multimodal large language models: A survey
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Brave: Broadening the visual encoding of vision-language models
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Keep the cost down: A review on methods to optimize llm’s kv-cache consumption
Shi Luohe, Zhang Hongyi, Yao Yao, Li Zuchao, and Zhao Hai · 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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Less is more: A simple yet effective token reduction method for efficient multi-modal llms
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Tidaldecode: Fast and accurate llm decoding with position persistent sparse attention
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Voco-llama: Towards vision compression with large language models
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Aligngpt: Multi-modal large language models with adaptive alignment capability
Fei Zhao, Taotian Pang, Chunhui Li, Zhen Wu, Junjie Guo, Shangyu Xing, and Xinyu Dai · 2024
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