QATM: Quality-Aware Template Matching For Deep Learning
This addresses template matching for computer vision applications, offering a novel approach that can be embedded into deep neural networks.
The paper tackles the problem of template matching in computer vision by proposing QATM, a quality-aware method that assesses matching pairs using soft-ranking, and it outperforms state-of-the-art methods and improves deep network solutions in evaluations.
Finding a template in a search image is one of the core problems many computer vision, such as semantic image semantic, image-to-GPS verification \etc. We propose a novel quality-aware template matching method, QATM, which is not only used as a standalone template matching algorithm, but also a trainable layer that can be easily embedded into any deep neural network. Specifically, we assess the quality of a matching pair using soft-ranking among all matching pairs, and thus different matching scenarios such as 1-to-1, 1-to-many, and many-to-many will be all reflected to different values. Our extensive evaluation on classic template matching benchmarks and deep learning tasks demonstrate the effectiveness of QATM. It not only outperforms state-of-the-art template matching methods when used alone, but also largely improves existing deep network solutions.