English(EN) SAKE: Self-aware Knowledge Exploitation-Exploration for Grounded Multimodal Named Entity Recognition

SAKE框架通过自感知知识利用增强多模态命名实体识别

作者 PulseAugur 编辑部 · [1 个来源] · 2026-04-22 03:17

研究人员开发了SAKE，一个旨在改进基础多模态命名实体识别（GMNER）的新框架。SAKE通过结合内部知识利用和外部知识探索，解决了开放世界环境中识别长尾和演化实体等挑战。该框架采用两阶段训练过程，包括难度感知搜索标签生成和代理强化学习，以实现工具调用的自感知决策。 AI

影响为GMNER引入了一种新颖的代理框架，有可能改进复杂、开放世界数据集中的实体识别。

排序理由这是一篇详细介绍特定AI任务新框架的研究论文。

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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Hugging Face Daily Papers TIER_1 English(EN) · 2026-04-22 03:17

SAKE：用于基础多模态命名实体识别的自感知知识利用-探索

Grounded Multimodal Named Entity Recognition (GMNER) aims to extract named entities and localize their visual regions within image-text pairs, serving as a pivotal capability for various downstream applications. In open-world social media platforms, GMNER remains challenging due …