PEPL: Precision-Enhanced Pseudo-Labeling for Fine-Grained Image Classification in Semi-Supervised Learning
<|reference_start|>PEPL: Precision-Enhanced Pseudo-Labeling for Fine-Grained Image Classification in Semi-Supervised Learning: Fine-grained image classification has witnessed significant advancements with the advent of deep learning and computer vision technologies. However, the scarcity of detailed annotations remains a major challenge, especially in scenarios where obtaining high-quality labeled data is costly or time-consuming. To address this limitation, we introduce Precision-Enhanced Pseudo-Labeling(PEPL) approach specifically designed for fine-grained image classification within a semi-supervised learning framework. Our method leverages the abundance of unlabeled data by generating high-quality pseudo-labels that are progressively refined through two key phases: initial pseudo-label generation and semantic-mixed pseudo-label generation. These phases utilize Class Activation Maps (CAMs) to accurately estimate the semantic content and generate refined labels that capture the essential details necessary for fine-grained classification. By focusing on semantic-level information, our approach effectively addresses the limitations of standard data augmentation and image-mixing techniques in preserving critical fine-grained features. We achieve state-of-the-art performance on benchmark datasets, demonstrating significant improvements over existing semi-supervised strategies, with notable boosts in accuracy and robustness.Our code has been open sourced at https://github.com/TianSuya/SemiFG.<|reference_end|>
@article{tian2024pepl:,
    title={PEPL: Precision-Enhanced Pseudo-Labeling for Fine-Grained Image
  Classification in Semi-Supervised Learning},
    author={Bowen Tian, Songning Lai, Lujundong Li, Zhihao Shuai, Runwei Guan,
  Tian Wu, Yutao Yue},
    journal={arXiv preprint arXiv:2409.03192},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.03192},
    primaryClass={cs.CV}
}