论文标题
自我EMD：没有Imagenet的自我监督对象检测
Self-EMD: Self-Supervised Object Detection without ImageNet
论文作者
论文摘要
在本文中，我们提出了一种新颖的自我监督的表示方法，即自我EMD，以进行对象检测。我们的方法直接在未标记的非标记图像数据集（例如可可）上训练，而不是像Imagenet这样的常用标志性观察图像数据集。我们将卷积特征图保持为嵌入图像以保持空间结构的嵌入，并采用地球移动器的距离（EMD）来计算两个嵌入之间的相似性。我们更快的R-CNN（RESNET50-FPN）基线在可可上获得了39.8％的地图，该地图与在Imagenet上预先训练的最先进的自我监督方法相当。更重要的是，它可以通过更无标记的图像进一步提高到40.4％的地图，这表明其利用更容易获得未标记的数据的巨大潜力。代码将提供。
In this paper, we propose a novel self-supervised representation learning method, Self-EMD, for object detection. Our method directly trained on unlabeled non-iconic image dataset like COCO, instead of commonly used iconic-object image dataset like ImageNet. We keep the convolutional feature maps as the image embedding to preserve spatial structures and adopt Earth Mover's Distance (EMD) to compute the similarity between two embeddings. Our Faster R-CNN (ResNet50-FPN) baseline achieves 39.8% mAP on COCO, which is on par with the state of the art self-supervised methods pre-trained on ImageNet. More importantly, it can be further improved to 40.4% mAP with more unlabeled images, showing its great potential for leveraging more easily obtained unlabeled data. Code will be made available.
