Conditional Link Prediction of Category-Implicit Keypoint Detection

• URL: http://arxiv.org/abs/2011.14462v1
• Date: Sun, 29 Nov 2020 23:00:37 GMT
• Title: Conditional Link Prediction of Category-Implicit Keypoint Detection
• Authors: Ellen Yi-Ge, Rui Fan, Zechun Liu, Zhiqiang Shen
• Abstract summary: We propose an end-to-end category-implicit Keypoint and Link Prediction Network (KLPNet) In our KLPNet, a novel Conditional Link Prediction Graph is proposed for link prediction among keypoints that are contingent on a predefined category. Experiments conducted on three publicly available benchmarks demonstrate that our KLPNet consistently outperforms all other state-of-the-art approaches.
• Score: 26.400925420154245
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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