Abstract
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.