Learning visual groups from co-occurrences in space and time
This work addresses the problem of unsupervised visual grouping for computer vision researchers, offering incremental improvements in specific domains.
The authors tackled the problem of learning visual groups from co-occurrences in space and time using a self-supervised framework, resulting in competitive object proposals, movie scene segmentations correlating with DVD chapters, and groups relating to semantic place categories.
We propose a self-supervised framework that learns to group visual entities based on their rate of co-occurrence in space and time. To model statistical dependencies between the entities, we set up a simple binary classification problem in which the goal is to predict if two visual primitives occur in the same spatial or temporal context. We apply this framework to three domains: learning patch affinities from spatial adjacency in images, learning frame affinities from temporal adjacency in videos, and learning photo affinities from geospatial proximity in image collections. We demonstrate that in each case the learned affinities uncover meaningful semantic groupings. From patch affinities we generate object proposals that are competitive with state-of-the-art supervised methods. From frame affinities we generate movie scene segmentations that correlate well with DVD chapter structure. Finally, from geospatial affinities we learn groups that relate well to semantic place categories.