  This paper presents a new self-supervised system for learning to detect novel
and previously unseen categories of objects in images. The proposed system
receives as input several unlabeled videos of scenes containing various
objects. The frames of the videos are segmented into objects using depth
information, and the segments are tracked along each video. The system then
constructs a weighted graph that connects sequences based on the similarities
between the objects that they contain. The similarity between two sequences of
objects is measured by using generic visual features, after automatically
re-arranging the frames in the two sequences to align the viewpoints of the
objects. The graph is used to sample triplets of similar and dissimilar
examples by performing random walks. The triplet examples are finally used to
train a siamese neural network that projects the generic visual features into a
low-dimensional manifold. Experiments on three public datasets, YCB-Video,
CORe50 and RGBD-Object, show that the projected low-dimensional features
improve the accuracy of clustering unknown objects into novel categories, and
outperform several recent unsupervised clustering techniques.
