  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.
