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English(EN) Jag: Direct Box Prediction for Efficient Visual Grounding

Jag模型实现直接边界框预测,提升视觉定位效率

研究人员开发了一种新颖的视觉定位方法Jag,该方法直接预测边界框坐标,而非顺序生成。该方法通过适配一个紧凑的预训练多模态模型,并添加一个轻量级回归头,使其能够同时输出四个坐标。通过应用几何监督,Jag同时优化了多模态表示和坐标映射,从而提高了准确性和效率。与现有的专用定位模型相比,该系统在较低的延迟和内存使用量下实现了具有竞争力的准确性。 AI

影响 这项研究可能带来更高效、更准确的视觉定位系统,影响需要图像内精确对象定位的应用。

排序理由 该集群包含一篇详细介绍新模型和方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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

Jag模型实现直接边界框预测,提升视觉定位效率

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该集群包含一篇详细介绍新模型和方法的学术论文。[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) · Xiuyuan Zhu, Ke Lu, Hao Wu, Zijin Du, Dongming Zhang, Jian Xue ·

    Jag: 用于高效视觉定位的直接边界框预测

    arXiv:2610.13239v1 Announce Type: new Abstract: Visual grounding combines image--language understanding with geometric prediction, yet autoregressive multimodal models often express a box as a coordinate sequence. Sequential decoding adds computation to the multimodal processing …