English(EN) From What to Which: Decoding Modifier Grounding in Frozen MLLMs

新的OTTER探针解码冻结多模态大语言模型中的视觉定位

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-09 04:00

研究人员开发了一种名为OTTER的新方法，用于探测冻结的多模态大语言模型（MLLMs）并从修饰语标记中解码定位信息。该技术使用最优传输来将生成的标记与特定的视觉区域对齐，从而创建定位图。研究发现，实例区分性的视觉信息，特别是对于空间术语，可以从这些修饰语中解码出来，并且即使在上下文受到干扰或跨不同数据集时，这种定位仍然具有信息量。 AI

影响这项研究可以改进MLLMs在视觉场景中理解和指代特定对象和属性的方式。

排序理由该集群包含一篇详细介绍分析MLLMs新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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Newsworthiness bucket: Tool; 该集群包含一篇详细介绍分析MLLMs新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Barbara Toniella Corradini (AI for Good), Caterina Gallegati (University of Siena, Italy), Ludovica Genovese (AI for Good), Vittorio Murino (AI for Good) · 2026-10-09 04:00

从“什么”到“哪个”：解码冻结多模态大模型中的修饰语归因

arXiv:2610.12305v1 Announce Type: new Abstract: As Multimodal Large Language Models (MLLMs) can describe increasingly complex visual scenes, token-level grounding becomes crucial. Yet, when an MLLM generates "the yellow banana on the left", established grounding approaches focus …

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报道来源 [1]

从“什么”到“哪个”：解码冻结多模态大模型中的修饰语归因

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