English(EN) GAVAGAI: Referential Alignment for Infant-Scale Vision-Language Models via Balanced Optimal Transport

新的GAVAGAI方法增强了视觉语言模型的接地能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-04 05:00

研究人员开发了一种名为GAVAGAI的新方法，以改进婴儿级视觉语言模型学习单词与图片关联的方式。该方法引入了一个代表单词到区域对齐的变量，并使用带有约束的平衡最优输运来确保单词被分配到相关区域，并处理单词不指代可见对象的情况。当与标准的字幕模型集成时，GAVAGAI在SAYCam婴儿视频数据集上显著提高了单词到图片准确性和定位性能。 AI

影响提高了视觉语言模型的接地能力，可能导致更可靠的单词到对象关联。

排序理由该集群包含一篇详细介绍视觉语言模型新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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arXiv cs.CL TIER_1 English(EN) · Rajdeep Pal, Jotiraditya Banerjee, Saptarshi Pani · 2026-11-04 05:00

GAVAGAI：通过平衡最优传输实现婴儿级视觉语言模型的参照对齐

arXiv:2611.00319v1 Announce Type: new Abstract: A child learns which words name which things from roughly 100 million words of input. Vision-language models trained at a comparable scale bind words to referents far less reliably, and we argue that part of the gap is structural: t…

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GAVAGAI：通过平衡最优传输实现婴儿级视觉语言模型的参照对齐

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