English(EN) ReferFlow: Beyond Final-Target Localization for Complex Referring Expressions

ReferFlow 为多模态大模型引入轨迹级别指代理解

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员推出 ReferFlow，这是一种新颖的方法，旨在改进多模态大语言模型在计算机视觉任务中处理复杂指代表达式的方式。与以往只关注最终目标的方法不同，ReferFlow 通过评估模型在连续地面化步骤中定位中间视觉引用的能力来解决轨迹级别的指代表达式理解问题。随附的 ReferFlow-Bench 基准测试旨在测试此能力，揭示当前模型在一致的中间地面化方面存在困难。ReferFlow 将轨迹生成与坐标解码分离，采用状态化区域潜在推理和轨迹感知并行解码来保留区域信息并有效细化坐标。 AI

影响增强了多模态大语言模型在复杂视觉地面化任务中的能力，可能改进它们对顺序指令的理解。

排序理由这是一篇详细介绍计算机视觉中指代表达式理解新方法和基准的 연구 논문。 [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Tianyi Gao, Han Fang, Xin Wei, Yaoyao Zhong, Zhaoyan Pan, Weijie Zhu, Hongbo Sun, Zhiyu Lin, Kaicheng Yang, Jinglin Xu, Ye Yuan, Hao Li, Hao Sun, Jingmin Xin · 2026-10-15 04:00

ReferFlow：超越最终目标定位，处理复杂指代表达式

arXiv:2610.20352v1 Announce Type: new Abstract: Multimodal Large Language Models have made significant progress in referring expression comprehension (REC), yet most REC benchmarks still focus on final-target localization. Complex queries often involve intermediate visual referen…

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报道来源 [1]

ReferFlow：超越最终目标定位，处理复杂指代表达式

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