| Abstrakt: |
This document is a guest editorial for a special issue of the CAAI Transactions on Intelligence Technology journal. The special issue focuses on advances in representation learning for computer vision. The editorial highlights the success of deep learning methods in deriving powerful representations from visual data, but also acknowledges the challenges of conducting representation learning with deep models, especially with large and noisy datasets. The document provides summaries of several research papers included in the special issue, covering topics such as cancellable biometrics, medical image analysis, watermarking for medical images, facial pattern description, multi-biometric strategies, semantic segmentation, image enhancement, image classification, and hyperspectral image super-resolution. The authors express their hope that these papers will enhance readers' understanding of current trends and guide future research in the field. The document also includes brief biographies of the authors. [Extracted from the article] |
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