论文标题
从按键到通过自我训练对应关系到地标：一种无监督地标发现的新方法
From Keypoints to Object Landmarks via Self-Training Correspondence: A novel approach to Unsupervised Landmark Discovery
论文作者
论文摘要
本文提出了一种新颖的范式，用于对物体地标探测器的无监督学习。与现有的方法相反，基于图像产生或均衡性等辅助任务的方法，我们提出了一种自我训练的方法，在该方法中，与通用关键点不同，地标探测器和描述符经过训练以改善自身，并将关键点调整为独特的地标。为此，我们提出了一种迭代算法，该算法在通过特征群集生成新的伪标签和通过对比学习为每个伪级学习的独特特征之间交替。凭借用于地标探测器和描述符的共享主链，KePoint位置逐渐收敛到稳定的地标，从而过滤量不太稳定的位置。与以前的作品相比，我们的方法可以学习捕获大型观点变化方面更灵活的点。我们在各种困难的数据集上验证了我们的方法，包括LS3D，BBCPOSE，HUMAN 36M和PENNATICE，并实现了新的最新结果。
This paper proposes a novel paradigm for the unsupervised learning of object landmark detectors. Contrary to existing methods that build on auxiliary tasks such as image generation or equivariance, we propose a self-training approach where, departing from generic keypoints, a landmark detector and descriptor is trained to improve itself, tuning the keypoints into distinctive landmarks. To this end, we propose an iterative algorithm that alternates between producing new pseudo-labels through feature clustering and learning distinctive features for each pseudo-class through contrastive learning. With a shared backbone for the landmark detector and descriptor, the keypoint locations progressively converge to stable landmarks, filtering those less stable. Compared to previous works, our approach can learn points that are more flexible in terms of capturing large viewpoint changes. We validate our method on a variety of difficult datasets, including LS3D, BBCPose, Human3.6M and PennAction, achieving new state of the art results.
