论文标题
Grafit：用粗标签学习细粒度的图像表示
Grafit: Learning fine-grained image representations with coarse labels
论文作者
论文摘要
本文解决了学习比培训标签提供的更精细表示的问题。这可以在仅带有粗标签注释的集合中的图像进行细粒类别检索。 我们的网络以最近的邻居分类器目标学习，并受到自我监督学习的启发的实例损失。通过共同利用粗糙标签和潜在的细粒潜在空间，它显着提高了类别水平检索方法的准确性。 我们的策略的表现优于所有竞争方法，用于在精细的粒度上检索或分类图像，而不是在火车时提供的图像。它还提高了将学习任务转移到细粒度数据集的准确性，从而在五个公共基准上建立了新的最新技术，例如Inaturalist-2018。
This paper tackles the problem of learning a finer representation than the one provided by training labels. This enables fine-grained category retrieval of images in a collection annotated with coarse labels only. Our network is learned with a nearest-neighbor classifier objective, and an instance loss inspired by self-supervised learning. By jointly leveraging the coarse labels and the underlying fine-grained latent space, it significantly improves the accuracy of category-level retrieval methods. Our strategy outperforms all competing methods for retrieving or classifying images at a finer granularity than that available at train time. It also improves the accuracy for transfer learning tasks to fine-grained datasets, thereby establishing the new state of the art on five public benchmarks, like iNaturalist-2018.
