This article presents two frameworks for combating issues with fine-grained vehicle recognition (FGVR) caused by image noise. The first, a progressive multi-task anti-noise learning (PMAL) framework, improves recognition accuracy by…
Continue readingThe study introduces two frameworks, the Progressive Multi-task Anti-noise Learning (PMAL) and Progressive Multi-task Distilling (PMD), to tackle the intra-class variation issue in Fine-grained Vehicle Recognition (FGVR) caused by image…
Continue readingThe article discusses a case study on annotating real estate images using a semi-supervised learning approach. The authors propose a model for automatic pre-selection of images from large unlabeled datasets…
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