Uni-MoE模型采用了统一的优化策略,使得模型能够方便地扩展至新的模态和任务。只需添加新的特征提取器和专家网络,即可实现对新模态的支持和扩展。 Uni-MoE模型的应用前景 Uni-MoE模型在多模态学习领域具有广泛的应用前景,主要包括以下几个方面: 1. 视觉与文本联合分析 在图像识别、文本分类等任务中,Uni-MoE模型可以...
总之,Uni-MoE 代表了多模态学习和人工智能领域向前迈出的重要一步。其创新方法利用 Mixture of Experts (MoE) 架构和战略性的三阶段训练策略,解决了传统方法的局限性,并在不同模态中实现了增强的性能、效率和泛化能力。在包括 ActivityNet-QA、RACE-Audio 和 A-OKVQA 在内的各种评估基准测试中取得的令人印象深刻的...
通过这种方式,Uni-MoE 可以理解多种模态,例如音频、语音、图像、视频和文本,在推理中只激活部分参数。 此外,为了增强 Uni-MoE 的多专家协作和泛化,开发一种三阶段渐进式训练方法:首先,分别使用大量图像/语音/音频到语言对来训练相应的连接器,实现 LLM 语言空间中的统一模态表示。其次,用跨模态数据集分别训练特定模...
Uni-MoE.github.io Public HTML 79 contributions in the last year Contribution Graph Day of Week October Oct November Nov December Dec January Jan February Feb March Mar April Apr May May June Jun July Jul August Aug September Sep October Oct Sunday Sun Monday Mon Tuesday Tue Wednesda...
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Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts If you appreciate our project, please consider giving us a star ⭐ on GitHub to stay updated with the latest developments. 🚀 Welcome to the repo of Uni-MOE! Uni-MoE is a MoE-based unified multimodal model and can handl...
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