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English(EN) Vague2Detect: Handling Ambiguous Prompts in Knowledge-Based Open-World Detection

Vague2Detect 通过模糊提示改进物体检测

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了 Vague2Detect，这是一个旨在提高开放世界物体检测模型在面对模糊或功能性提示时的准确性的新流程。与 YOLO-World 等在模糊语言方面存在困难的现有模型不同，Vague2Detect 集成了知识库和经过微调的 Sentence-BERT 模型，以更好地解释用户查询。对于知识库未涵盖的提示，它利用 GPT-3.5 Turbo 动态地用新概念扩展知识库。这种方法显著提高了模糊提示成功率 (VPSR)，在家庭场景的基准测试中达到 61%，并且在使用 GPT 回退时高达 85%。 AI

影响 增强了人工智能系统理解和响应细微、现实世界用户指令的能力。

排序理由 该集群包含一篇详细介绍改进物体检测新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

Vague2Detect 通过模糊提示改进物体检测

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该集群包含一篇详细介绍改进物体检测新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Ibrohimjon Muminov (Dongguk University, Seoul, South Korea), Jihie Kim (Dongguk University, Seoul, South Korea) ·

    Vague2Detect：处理知识型开放世界检测中的模糊提示

    arXiv:2609.09949v1 Announce Type: cross Abstract: Real-world detectors must often interpret functional or ambiguous prompts, yet conventional models such as YOLO remain restricted to fixed class lists. Even open-vocabulary models like YOLO-World frequently misalign vague language…

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