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

Jag模型提供高效的直接边界框预测用于视觉定位

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-09 00:00

研究人员推出Jag，一种新颖的视觉定位模型，旨在实现高效且直接的边界框预测。与逐个顺序解码坐标的传统自回归模型不同，Jag使用轻量级的回归头在单次前向传播中同时预测所有四个边界框坐标。与现有的专用模型相比，这种方法不仅加快了推理速度，还减少了GPU内存使用量。Jag在各种基准测试和目标尺寸上都显示出改进的定位精度，证明了多模态表示和几何监督联合适应的有效性。 AI

影响 Jag的高效直接边界框预测可以加速需要多模态AI系统中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及其性能的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 Hugging Face Daily Papers 阅读 →

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

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报道来源 [1]

Hugging Face Daily Papers TIER_1 English(EN) · 2026-10-09 00:00

Jag：高效视觉基础的直接盒子预测

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 needed for localization. Adapting these represen…

本文如何被排名

报道来源 [1]

Jag：高效视觉基础的直接盒子预测

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