**Unsupervised Discovery of Object Landmarks via Contrastive Learning** [Zezhou Cheng](http://people.cs.umass.edu/~zezhoucheng), [Jong-Chyi Su](https://people.cs.umass.edu/~jcsu/), [Subhransu Maji](http://people.cs.umass.edu/~smaji/) _University of Massachusetts - Amherst_
Given a collection of images, humans are able to discover landmarks of the depicted objects by modeling the shared geometric structure across instances. This idea of geometric equivariance has been widely used for unsupervised discovery of object landmark representations. In this paper, we develop a simple and effective approach based on contrastive learning of invariant representations. We show that when a deep network is trained to be invariant to geometric and photometric transformations, representations from its intermediate layers are highly predictive of object landmarks. Furthermore, by stacking representations across layers in a hypercolumn their effectiveness can be improved. Our approach is motivated by the phenomenon of the gradual emergence of invariance in the representation hierarchy of a deep network. We also present a unified view of existing equivariant and invariant representation learning approaches through the lens of contrastive learning, shedding light on the nature of invariances learned. Experiments on standard benchmarks for landmark discovery, as well as a challenging one we propose, show that the proposed approach surpasses prior state-of-the-art. Publication ========================================================================================== **Unsupervised Discovery of Object Landmarks via Contrastive Learning**
Zezhou Cheng, Jong-Chyi Su, Subhransu Maji
_arXiv:2006.14787_, 2020.
[[arXiv](https://arxiv.org/abs/2006.14787)] Code ========================================================================================== [GitHub Link](https://github.com/cvl-umass/ContrastLandmark) Birds benchmark ========================================================================================== We collect a challenging dataset of birds where objects appear in clutter, occlusion, and exhibit wider pose variation. It contains 100K images randomly sampled from [iNat 2017 dataset](https://github.com/visipedia/inat_comp/tree/master/2017) under the class "Aves" for unsupervised representation learning and 2006 images from [CUB-200-2011](http://www.vision.caltech.edu/visipedia/CUB-200-2011.html) for landmark regression. Here are the lists of images: [[iNat Aves 100K](datasets/inat_aves_100K.txt)] [[CUB train/val/test set](datasets/cub_filelist.zip)] Some examples are as follows:
Acknowledgements ========================================================================================== The project is supported in part by Grants #1661259 and #1749833 from the National Science Foundation of United States. Our experiments were performed on the University of Massachusetts, Amherst GPU cluster obtained under the Collaborative Fund managed by the Massachusetts Technology Collaborative.