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About Learning To Compare Image Patches Via Convolutional Neural Networks
This document proposes using convolutional neural networks (CNNs) to learn a general similarity function for comparing image patches. The authors explore different CNN architectures specifically adapted for this task and trained directly on raw image patch data without manually designed features. They show their CNN-based approach outperforms existing methods on several benchmark datasets, providing more efficient patch descriptors. The key contributions are learning a patch similarity function directly from data using CNNs, exploring effective network models, and demonstrating improved performance over hand-crafted descriptors and other learned approaches.
Detailed Information
| Author: | ['Siva Jyothi Chandra'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 9 |
| Language: | English |
| Format: | |
| Price: | FREE |
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