通用视觉自监督研究报
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2026-05-29 图像表征 · VFM · JEPA · 视频预训练
Visual SSL / representation · P2 · 2026-05-29

Look on Demand:它和通用视觉自监督的关系在于:让语言模型决定何时调用视觉感知模块,适合观察“视觉证据何时进入推理链”

中相关;详见方法、贡献和实验边界。

编号2605.28160 优先级P2 类别Visual SSL / representation 会议arXiv 方法让语言模型决定何时调用视觉感知模块,适合观察“视觉证据何时进入推理链” 来源arXiv / OpenReview

先说结论。它和通用视觉自监督的关系在于:让语言模型决定何时调用视觉感知模块,适合观察“视觉证据何时进入推理链”。

Figureure 1 · Illustration of two dominant multimodal reasoning paradigms and our fr
Figureure 1 · Illustration of two dominant multimodal reasoning paradigms and our frFigure 1. Illustration of two dominant multimodal reasoning paradigms and our framework.这张图概括 Look on Demand 的整体方法流程。阅读时先看模块之间传递的训练信号,再看作者如何把目标拆成可优化的子问题。
Figureure 3 · Overview of the CSMR architecture and its reasoning workflow
Figureure 3 · Overview of the CSMR architecture and its reasoning workflowFigure 3. Overview of the CSMR architecture and its reasoning workflow. The left panel illustrates the overall structure of the CSMR, which consists of a CRC and a PVP. Given an input image and a question, the CRC maintains the current reasoning state and generates targeted visual queries to invoke the PVP when necessary. The PVP independently analyzes the original image and returns textualized visual evidence that answers the issued query. This evidence is then integrated into the CRC’s reasoning state to support subsequent reasoning. The right panel presents a concrete example of reasoning. The CRC progressively generates visual queries based on the current reasoning state. Once the obtained textualized visual evidence is deemed sufficient, the CRC directly produces the final answer.这张图概括 Look on Demand 的整体方法流程。阅读时先看模块之间传递的训练信号,再看作者如何把目标拆成可优化的子问题。

核心问题

它和通用视觉自监督的关系在于:让语言模型决定何时调用视觉感知模块,适合观察“视觉证据何时进入推理链”。

方法拆解

让语言模型决定何时调用视觉感知模块,适合观察“视觉证据何时进入推理链”

主要贡献

中相关;详见方法、贡献和实验边界。

实验看点

实验部分建议重点看两类证据:一是作者是否把方法收益和更强数据、更长训练、更大模型区分开;二是跨模型、跨数据或跨任务迁移是否还能保留同样趋势。

局限与读法

这篇论文的结论需要结合任务设置、训练数据规模和消融实验一起看;不要只凭单个指标判断它对通用视觉表征的价值。