Patch-Level Consistency Regularization in Self-Supervised Transfer Learning for Fine-Grained Image Recognition
Yejin Lee,
Suho Lee,
Sangheum Hwang
Abstract:Fine-grained image recognition aims to classify fine subcategories belonging to the same parent category, such as vehicle model or bird species classification. This is an inherently challenging task because a classifier must capture subtle interclass differences under large intraclass variances. Most previous approaches are based on supervised learning, which requires a large-scale labeled dataset. However, such large-scale annotated datasets for fine-grained image recognition are difficult to collect because … Show more
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