1,272 research outputs found
Interspecies Knowledge Transfer for Facial Keypoint Detection
We present a method for localizing facial keypoints on animals by
transferring knowledge gained from human faces. Instead of directly finetuning
a network trained to detect keypoints on human faces to animal faces (which is
sub-optimal since human and animal faces can look quite different), we propose
to first adapt the animal images to the pre-trained human detection network by
correcting for the differences in animal and human face shape. We first find
the nearest human neighbors for each animal image using an unsupervised shape
matching method. We use these matches to train a thin plate spline warping
network to warp each animal face to look more human-like. The warping network
is then jointly finetuned with a pre-trained human facial keypoint detection
network using an animal dataset. We demonstrate state-of-the-art results on
both horse and sheep facial keypoint detection, and significant improvement
over simple finetuning, especially when training data is scarce. Additionally,
we present a new dataset with 3717 images with horse face and facial keypoint
annotations.Comment: CVPR 2017 Camera Read
Unsupervised learning of object landmarks by factorized spatial embeddings
Learning automatically the structure of object categories remains an
important open problem in computer vision. In this paper, we propose a novel
unsupervised approach that can discover and learn landmarks in object
categories, thus characterizing their structure. Our approach is based on
factorizing image deformations, as induced by a viewpoint change or an object
deformation, by learning a deep neural network that detects landmarks
consistently with such visual effects. Furthermore, we show that the learned
landmarks establish meaningful correspondences between different object
instances in a category without having to impose this requirement explicitly.
We assess the method qualitatively on a variety of object types, natural and
man-made. We also show that our unsupervised landmarks are highly predictive of
manually-annotated landmarks in face benchmark datasets, and can be used to
regress these with a high degree of accuracy.Comment: To be published in ICCV 201
- …