论文标题
颗粒：使用无监督学习的置信度识别的零件识别
PARTICUL: Part Identification with Confidence measure using Unsupervised Learning
论文作者
论文摘要
在本文中，我们提出了一种颗粒，这是一种用于从细粒识别中使用的数据集中无监督学习零件检测器的新型算法。它利用了训练集中所有图像的宏相似性，以便在预先训练的卷积神经网络的特征空间中进行重复的模式。我们提出了实现检测部件的局部性和统一性的新目标功能。此外，我们根据相关评分将检测器嵌入了置信度度量，从而允许系统估计每个部分的可见性。我们将方法应用于两个公共细粒数据集（Caltech-UCSD Bird 200和Stanford Cars），并表明我们的探测器可以一致地突出该物体的一部分，同时很好地衡量了对其预测的信心。我们还证明，这些探测器可直接用于构建基于零件的细粒分类器，这些分类器在基于原型的方法的透明度与非解剖方法的性能之间提供了良好的折衷。
In this paper, we present PARTICUL, a novel algorithm for unsupervised learning of part detectors from datasets used in fine-grained recognition. It exploits the macro-similarities of all images in the training set in order to mine for recurring patterns in the feature space of a pre-trained convolutional neural network. We propose new objective functions enforcing the locality and unicity of the detected parts. Additionally, we embed our detectors with a confidence measure based on correlation scores, allowing the system to estimate the visibility of each part. We apply our method on two public fine-grained datasets (Caltech-UCSD Bird 200 and Stanford Cars) and show that our detectors can consistently highlight parts of the object while providing a good measure of the confidence in their prediction. We also demonstrate that these detectors can be directly used to build part-based fine-grained classifiers that provide a good compromise between the transparency of prototype-based approaches and the performance of non-interpretable methods.
