5月17日,鹅厂协同国内几大高校实验室发布了一篇有关多模态大模型的综述文章《Efficient Multimodal Large Language Models: A Survey》,有广度有深度地介绍了多模态大模型的行业发展现状,对多模态大模型发展感觉兴趣的同学觉得有用就一键三连吧~ *本文只摘译精华部分,需要了解全文的请至文末跳转至原文链接阅读。 *楼主
全文:Efficient Multimodal Large Language Models - Jinxiang Lai - 20240618目录TimelineArchitectureTrainingData and Benchmarks内容概览
In the past year, Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance in tasks such as visual question answering, visual understanding and reasoning. However, the extensive model size and high training and inference costs have hindered the widespread application of MLLMs ...
Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models D. Driess PaLM-E: an embodied multimodal language model (2023)View more references Cited by (0)1 Code and data are available at https://github.com/SabbaghCodes/MiniMedGPT.git.View...
OpenFlamingo - An open-source framework for training large multimodal models RoboFlamingo - Vision-Language Foundation Models as Effective Robot Imitators Notes The code has been reorganized, which may lead to errors or deviations from the original research results. If you encounter any issues, pleas...
On Speculative Decoding for Multimodal Large Language Models AdaEDL: Early Draft Stopping for Speculative Decoding of Large Language Models via an Entropy-based Lower Bound on Token Acceptance Probability Your Apps Are on Borrowed Time. AI Agents Are on the Way Qualcomm AI Engine for Snapdragon 8 ...
1 Dec 2024·Wenxuan Huang,Zijie Zhai,Yunhang Shen,Shaosheng Cao,Fei Zhao,Xiangfeng Xu,Zheyu Ye,Shaohui Lin· Multimodal Large Language Models (MLLMs) have achieved remarkable success in vision understanding, reasoning, and interaction. However, the inference computation and memory increase progressive...
Large Multimodal Models (LMMs) have shown significant visual reasoning capabilities by connecting a visual encoder and a large language model. LMMs typically take in a fixed and large amount of visual tokens, such as the penultimate layer features in the CLIP visual encoder, as the prefix content...
Multimodal Large Language Models (MLLMs) have attracted much attention for their multifunctionality. However, traditional Transformer architectures incur significant overhead due to their secondary computational complexity. To address this issue, we introduce ML-Mamba, a multimodal language model, which util...
The advent of real-time large multimodal models (LMMs) like GPT-4o has sparked considerable interest in efficient LMMs. LMM frameworks typically encode visual inputs into vision tokens (continuous representations) and integrate them and textual instructions into the context of large language models (...