English(EN) ViewSAM: Learning View-aware Cross-modal Semantics for Weakly Supervised Cross-view Referring Multi-Object Tracking

ViewSAM模型利用基础模型进行弱监督跨视图目标跟踪

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-04 14:23

研究人员开发了ViewSAM，一个用于弱监督跨视图多目标跟踪（CRMOT）的新型框架。该方法利用SAM2和SAM3等基础模型生成伪监督，减少了对昂贵帧级标注的需求。ViewSAM显式地建模视图感知的跨模态语义，从而能够以最少的额外参数在不同的摄像机视角下进行鲁棒跟踪。 AI

影响通过减少对大量标注的依赖，为跨摄像机视图的多目标跟踪引入了一种更有效的方法。

排序理由该集群包含一篇研究论文，详细介绍了用于特定计算机视觉任务的新模型和框架。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

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arXiv cs.CV TIER_1 English(EN) · Jiawei Ge, Xintian Zhang, Jiuxin Cao, Bo Liu, Fabian Deuser, Chang Liu, Gong Wenkang, Siyou Li, Juexi Shao, Wenqing Wu, Chen Feng, Ioannis Patras · 2026-05-05 04:00

ViewSAM：学习视域感知跨模态语义用于弱监督跨视域指代多目标跟踪

arXiv:2605.02638v1 Announce Type: new Abstract: Cross-view Referring Multi-Object Tracking (CRMOT) aims to track multiple objects specified by natural language across multiple camera views, with globally consistent identities. Despite recent progress, existing methods rely heavil…
arXiv cs.CV TIER_1 English(EN) · Ioannis Patras · 2026-05-04 14:23

ViewSAM：学习视图感知跨模态语义用于弱监督跨视图多目标跟踪

Cross-view Referring Multi-Object Tracking (CRMOT) aims to track multiple objects specified by natural language across multiple camera views, with globally consistent identities. Despite recent progress, existing methods rely heavily on costly frame-level spatial annotations and …