Object category learning and retrieval with weak supervision
Abstract
We consider the problem of retrieving objects from image data and learning to
classify them into meaningful semantic categories with minimal supervision. To
that end, we propose a fully differentiable unsupervised deep clustering approach
to learn semantic classes in an end-to-end fashion without individual class labeling
using only unlabeled object proposals. The key contributions of our work are 1)
a kmeans clustering objective where the clusters are learned as parameters of the
network and are represented as memory units, and 2) simultaneously building a
feature representation, or embedding, while learning to cluster it. This approach
shows promising results on two popular computer vision datasets: on CIFAR10 for
clustering objects, and on the more complex and challenging Cityscapes dataset
for semantically discovering classes which visually correspond to cars, people, and
bicycles. Currently, the only supervision provided is segmentation objectness masks,
but this method can be extended to use an unsupervised objectness-based object
generation mechanism which will make the approach completely unsupervised.
Research Areas
