Abstract
Fine Grained Visual Categorization (FGVC) remains a challenging task in computer vision due to subtle inter class differences and fragile feature representations. Existing methods struggle in fine grained scenarios, especially when labeled data is scarce. We propose a semi supervised method combining Mamba based feature modeling, region attention, and Bayesian uncertainty. Our approach enhances local to global feature modeling while focusing on key areas during learning. Bayesian inference selects high quality pseudo labels for stability. Experiments show strong performance on FGVC benchmarks with occlusions, demonstrating robustness when labeled data is limited. Code is available at this https URL Net.
Abstract (translated)
细粒度视觉分类（FGVC）在计算机视觉领域中仍是一项挑战，原因是类别间的细微差异和脆弱的特征表示。现有的方法在细粒度场景下尤其挣扎，特别是在标记数据稀缺的情况下。我们提出了一种半监督方法，结合了基于Mamba的特征建模、区域注意力机制以及贝叶斯不确定性分析。我们的方法通过聚焦关键学习区域来增强从局部到全局的特征建模能力。此外，贝叶斯推理用于选择高质量的伪标签，以提高模型稳定性。实验显示，在有遮挡的情况下，本方法在细粒度视觉分类基准测试中表现出了强大的性能，并且当标记数据有限时具有较好的鲁棒性。代码可在以下网址获取：[此URL链接]（请将[此URL链接]替换为实际的代码仓库或演示网站地址）。
URL
https://arxiv.org/abs/2506.21905
