BiSTF: Bilateral-Branch Self-Training Framework for Semi-Supervised Large-scale Fine-Grained Recognition

• URL: http://arxiv.org/abs/2107.06768v1
• Date: Wed, 14 Jul 2021 15:28:54 GMT
• Title: BiSTF: Bilateral-Branch Self-Training Framework for Semi-Supervised Large-scale Fine-Grained Recognition
• Authors: Hao Chang, Guochen Xie, Jun Yu, Qiang Ling
• Abstract summary: Semi-supervised Fine-Grained Recognition is a challenge task due to data imbalance, high interclass similarity and domain mismatch. We propose Bilateral-Branch Self-Training Framework (BiSTF) to improve existing semi-balanced and domain-shifted fine-grained data. We show BiSTF outperforms the existing state-of-the-art SSL on Semi-iNat dataset.
• Score: 28.06659482245647
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semi-supervised Fine-Grained Recognition is a challenge task due to the difficulty of data imbalance, high inter-class similarity and domain mismatch. Recent years, this field has witnessed great progress and many methods has gained great performance. However, these methods can hardly generalize to the large-scale datasets, such as Semi-iNat, as they are prone to suffer from noise in unlabeled data and the incompetence for learning features from imbalanced fine-grained data. In this work, we propose Bilateral-Branch Self-Training Framework (BiSTF), a simple yet effective framework to improve existing semi-supervised learning methods on class-imbalanced and domain-shifted fine-grained data. By adjusting the update frequency through stochastic epoch update, BiSTF iteratively retrains a baseline SSL model with a labeled set expanded by selectively adding pseudo-labeled samples from an unlabeled set, where the distribution of pseudo-labeled samples are the same as the labeled data. We show that BiSTF outperforms the existing state-of-the-art SSL algorithm on Semi-iNat dataset.

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