论文标题
条件链接预测 - 图像点检测
Conditional Link Prediction of Category-Implicit Keypoint Detection
论文作者
论文摘要
对象的按键反映了它们的简洁抽象，而相应的连接链接（CL）通过检测关键点之间的内在关系来构建骨架。现有方法通常是计算密集型的，对于属于多个类的实例和/或同时编码连接信息不可行的实例不适用。为了解决上述问题，我们提出了一个端到端类别 - 限制关键点和链接预测网络（KLPNET），这是同时使用语义关键点检测（用于多类实例）和CL REJUFENATION的第一种方法。在我们的KLPNET中，提出了一个新颖的条件链接预测图，以供链接预测在预定义类别中的关键点之间。此外，引入了一个跨阶段关键点定位模块（CKLM），以探索用于粗到细胞点的特征聚合。对三个公开基准进行的全面实验表明，我们的KLPNET始终超过所有其他最先进的方法。此外，CL预测的实验结果还显示了我们KLPNET在遮挡问题方面的有效性。
Keypoints of objects reflect their concise abstractions, while the corresponding connection links (CL) build the skeleton by detecting the intrinsic relations between keypoints. Existing approaches are typically computationally-intensive, inapplicable for instances belonging to multiple classes, and/or infeasible to simultaneously encode connection information. To address the aforementioned issues, we propose an end-to-end category-implicit Keypoint and Link Prediction Network (KLPNet), which is the first approach for simultaneous semantic keypoint detection (for multi-class instances) and CL rejuvenation. In our KLPNet, a novel Conditional Link Prediction Graph is proposed for link prediction among keypoints that are contingent on a predefined category. Furthermore, a Cross-stage Keypoint Localization Module (CKLM) is introduced to explore feature aggregation for coarse-to-fine keypoint localization. Comprehensive experiments conducted on three publicly available benchmarks demonstrate that our KLPNet consistently outperforms all other state-of-the-art approaches. Furthermore, the experimental results of CL prediction also show the effectiveness of our KLPNet with respect to occlusion problems.
