GatedUniPose：一种结合UniRepLKNet与门控卷积的姿态估计新方法
GatedUniPose: A Novel Approach for Pose Estimation Combining UniRepLKNet and Gated Convolution
AI总结：
针对复杂场景下姿态估计精度不足的问题，提出结合UniRepLKNet与门控卷积的GatedUniPose方法，引入GLACE模块和DySample上采样优化，在多个数据集上以较少参数量实现了性能提升。
摘要：
姿态估计是计算机视觉中的一项关键任务，在自动驾驶、人体动作捕捉和虚拟现实领域有着广泛应用。然而现有方法仍面临精度提升的挑战，在复杂场景下尤为突出。本文提出一种新型姿态估计方法GatedUniPose，该方法结合UniRepLKNet与门控卷积，并引入GLACE模块进行特征嵌入。此外，我们通过使用DySample上采样改进了头部层的特征图拼接方式。与现有方法相比，GatedUniPose在处理复杂场景和遮挡挑战方面表现更优。在COCO、MPII和CrowdPose数据集上的实验结果表明，GatedUniPose以相对较少的参数量实现了显著的性能提升，其结果优于或堪比参数量相近或更大的模型。
英文摘要：
Pose estimation is a crucial task in computer vision, with wide applications in autonomous driving, human motion capture, and virtual reality. However, existing methods still face challenges in achieving high accuracy, particularly in complex scenes. This paper proposes a novel pose estimation method, GatedUniPose, which combines UniRepLKNet and Gated Convolution and introduces the GLACE module for embedding. Additionally, we enhance the feature map concatenation method in the head layer by using DySample upsampling. Compared to existing methods, GatedUniPose excels in handling complex scenes and occlusion challenges. Experimental results on the COCO, MPII, and CrowdPose datasets demonstrate that GatedUniPose achieves significant performance improvements with a relatively small number of parameters, yielding better or comparable results to models with similar or larger parameter sizes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
