  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.
