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.

本文介绍了一种半监督的方法，使用少量标记图像和大量未标记图像同时学习关键点热图和姿态不变的关键点表示，关键点表示的语义一致性约束加以保证，并通过特征空间将图像及其扩充副本的关键点表示紧密地联系在一起，以实现对于人类和动物身体特征点的本土化定位，并在多项基准测试中显著优于先前的方法。