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English(EN) ProCal: Inference-Time Proposal Calibration for Open-Vocabulary Object Detection

新ProCal方法增强开放词汇目标检测

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了ProCal，一种用于开放词汇目标检测的新颖方法，可在推理时校准分类分数。该方法通过分析预训练的视觉-语言模型（VLMs）区分前景和背景区域的能力来利用它们。ProCal结合了感知定位的前景分数和感知背景的抑制分数，以提高在训练期间未见过的类别的目标定位和分类的准确性。当应用于CLIPSelf ViT-L/14时，ProCal在OV-LVIS数据集上展示了+2.5 APr的显著改进。 AI

影响 提高了对未见类别目标检测的能力，可能增强图像分析和计算机视觉中的应用。

排序理由 该集群描述了一篇提出新颖目标检测方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新ProCal方法增强开放词汇目标检测

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该集群描述了一篇提出新颖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.AI TIER_1 English(EN) · Jae-Ryung Hong, Ho-Joong Kim, Seong-Whan Lee ·

    ProCal：面向开放词汇目标检测的推理时提议校准

    arXiv:2607.01759v1 Announce Type: cross Abstract: Open-vocabulary object detection aims to localize and classify objects beyond the fixed set of categories seen dur ing training. Recent open-vocabulary object detection methods improve localization and classification for unseen ca…

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