Keypoint Detection
Overview
Keypoint detection finds either repeatable local features for comparing images or meaningful landmarks on people and objects for estimating pose and location. Methods include SIFT-like features, learned detectors and descriptors, heatmap-based convolutional networks, and transformers; self-supervised training, geometric constraints, and prompts help reduce label needs or improve robustness. Human-pose systems are commonly evaluated on benchmarks such as COCO, while local matching is tested on HPatches and MegaDepth, with applications ranging from robotics to medical imaging. A central challenge is balancing accuracy and robustness to blur, occlusion, viewpoint, and domain shifts with inference speed, limited annotations, and reliable performance across settings.
Latest papers
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