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English(EN) Bridging Object Detection and Segmentation with Polygon Detection Transformers

新型 Poly-DETR 模型连接物体检测与分割

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种新颖的方法——多边形检测 Transformer (Poly-DETR)，它弥合了物体检测与分割之间的差距。该方法利用极坐标表示直接构建逼近轮廓的多边形，与传统的边界框或像素级掩码相比，提供了更紧凑、更准确的表示。Poly-DETR 与 DETR 类检测器无缝集成，并引入了极坐标可变形注意力 (Polar Deformable Attention) 和感知位置的训练方案 (Position-Aware Training Scheme) 等特定设计来提高性能。该模型在 MS COCO 数据集上取得了优异的成果，并在包括遥感和医学成像在内的各个领域的高分辨率场景应用中展现出潜力。 AI

影响 引入了一种新颖的物体表示方法，有望提高计算机视觉任务的准确性和效率。

排序理由 这是一篇详细介绍新模型架构和方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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新型 Poly-DETR 模型连接物体检测与分割

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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Jiacheng Sun, Jiaqi Lin, Wenlong Hu, Haoyang Li, Xinghong Zhou, Chenghai Mao, Xinliang Zhang, Jianya Guo, Yuqiang Zhai, Yan Peng, Xiaomao Li ·

    利用多边形检测 Transformer 连接目标检测与分割

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