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.

翻译：在本文中,我们展示了 " partICUL ",这是一个在不受监督的情况下从微小识别中所使用的数据集中学习部分探测器的新算法,它利用了成套培训中所有图像的宏观差异性,以便在经过预先训练的神经神经网络的特征空间中埋设重复模式;我们提出新的客观功能,以测量所探测到的部件的位置和独特性;此外,我们根据相关分数将我们的探测器嵌入一个信任度度,使系统能够估计每个部件的可见度;我们将我们的方法应用于两个公共精密数据集(Caltech-UCSD Bird 200和斯坦福汽车),并表明我们的探测器可以一贯地突出目标的部分内容,同时对它们的预测提供良好的信任度度;我们还表明,这些探测器可以直接用来建立部分基于精细的分类器,在原型方法的透明度与非解释方法的性能之间提供良好的妥协。