Knowledge about the locations of keypoints of an object in an image can assist in fine-grained classification and identification tasks, particularly for the case of objects that exhibit large variations in poses that greatly influence their visual appearance, such as wild animals. However, supervised training of a keypoint detection network requires annotating a large image dataset for each animal species, which is a labor-intensive task. To reduce the need for labeled data, we propose to learn simultaneously keypoint heatmaps and pose invariant keypoint representations in a semi-supervised manner using a small set of labeled images along with a larger set of unlabeled images. Keypoint representations are learnt with a semantic keypoint consistency constraint that forces the keypoint detection network to learn similar features for the same keypoint across the dataset. Pose invariance is achieved by making keypoint representations for the image and its augmented copies closer together in feature space. Our semi-supervised approach significantly outperforms previous methods on several benchmarks for human and animal body landmark localization.

中文翻译：

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半监督关键点本地化

有关对象在图像中关键点位置的知识可以帮助进行细粒度的分类和识别任务，特别是对于那些在姿态上表现出很大变化而大大影响其视觉外观的对象（例如野生动物）的情况。但是，对关键点检测网络的监督训练需要为每种动物注释大型图像数据集，这是一项劳动密集型任务。为了减少对标记数据的需求，我们建议同时学习关键点热图，并使用少量的标记图像集和较大的未标记图像集以半监督的方式构成不变的关键点表示。使用语义关键点一致性约束来学习关键点表示，该语义关键点一致性约束迫使关键点检测网络学习整个数据集中同一关键点的相似特征。通过使图像及其增强副本的关键点表示在特征空间中靠得更近，可以实现姿势不变性。在人和动物体界标定位的几个基准上，我们的半监督方法大大优于以前的方法。