研究人员发布了Modus，一种新颖的解码器式任意模态到任意模态多模态建模架构。该方法对称处理所有模态，允许在单个网络内实现任意输入和输出，无需专门的头部或损失函数。Modus在各种基准测试中表现出与现有专业和多任务模型相当的性能，所有相关材料均已开源。 AI
影响 引入了一种统一的解码器式多模态建模方法，有望简化并提高各种应用的性能。
排序理由 该集群描述了一篇关于新颖AI模型架构的详细研究论文。
在 Hugging Face Daily Papers 阅读 →
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
完整方法见我们的编辑标准。
arXiv:2607.25948v1 Announce Type: cross Abstract: Any-to-any models predict any modality from any combination of others within a single network, a formulation used in multimodal vision and vision-language models, and increasingly in scientific domains such as ecology and astronom…
Any-to-any models predict any modality from any combination of others within a single network, a formulation used in multimodal vision and vision-language models, and increasingly in scientific domains such as ecology and astronomy. Existing any-to-any models are typically traine…