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English(EN) NegAS: Negative Label Guided Attention and Scoring for Out-of-Distribution Object Detection with Vision-Language Models

新框架NegAS提升了视觉语言模型中分布外目标的检测能力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种新颖的框架NegAS，旨在增强视觉语言模型（VLMs）中分布外（OOD）目标的检测能力。NegAS解决了两个关键挑战：改进注意力机制以更好地识别潜在的OOD区域，以及开发与VLM概率输出兼容的评分函数。该框架利用负标签来指导注意力，并使用基于sigmoid的评分函数来区分分布内和分布外实例，在COCO和OpenImages等数据集上显著提高了OOD检测性能，同时保持了对分布内目标的准确性。 AI

影响 通过增强分布外检测能力，提高了AI系统在安全关键应用中的可靠性。

排序理由 详细介绍一种新目标检测方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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新框架NegAS提升了视觉语言模型中分布外目标的检测能力

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详细介绍一种新目标检测方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Yingjie Zhang, Shuai Li, Peng Wang ·

    NegAS：负标签引导的注意力与评分，用于视觉-语言模型的分布外目标检测

    arXiv:2606.22537v2 Announce Type: replace Abstract: Out-of-Distribution (OOD) detection is essential for ensuring the robustness and reliability of object detection systems deployed in safety-critical applications. While prior research has mainly focused on uni-modal detectors or…

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