论文标题
端到端学习Fisher矢量编码的零件特征，以细粒度识别
End-to-end Learning of a Fisher Vector Encoding for Part Features in Fine-grained Recognition
论文作者
论文摘要
基于零件的细粒识别方法并未表明与全球方法相比的预期性能增长，尽管明确关注与区分高度相似类别相关的小细节。我们假设基于零件的方法遭受了局部特征的缺失表示，这对零件的顺序不变，并且可以适当地处理不同数量的可见零件。零件的顺序是人造的，通常仅由地面真实注释给出，而视点变化和遮挡导致不可观察的部分。因此，我们建议将零件特征编码的Fisher矢量编码整合到卷积神经网络中。该编码的参数由与神经网络的在线EM算法共同估算，并且比以前的作品的估计更精确。我们的方法改善了三个鸟类分类数据集的最新精度。
Part-based approaches for fine-grained recognition do not show the expected performance gain over global methods, although explicitly focusing on small details that are relevant for distinguishing highly similar classes. We assume that part-based methods suffer from a missing representation of local features, which is invariant to the order of parts and can handle a varying number of visible parts appropriately. The order of parts is artificial and often only given by ground-truth annotations, whereas viewpoint variations and occlusions result in not observable parts. Therefore, we propose integrating a Fisher vector encoding of part features into convolutional neural networks. The parameters for this encoding are estimated by an online EM algorithm jointly with those of the neural network and are more precise than the estimates of previous works. Our approach improves state-of-the-art accuracies for three bird species classification datasets.
