Optical Flow boosts Unsupervised Localization and Segmentation

Understand

Unsupervised localization and segmentation are long-standing robot vision challenges that describe the critical ability for an autonomous robot to learn to decompose images into individual objects without labeled data.

• These tasks are important because of the limited availability of dense image manual annotation and the promising vision of adapting to an evolving set of object categories in lifelong learning.
• Most recent methods focus on using visual appearance continuity as object cues by spatially clustering features obtained from self-supervised vision transformers (ViT).
• In this work, we leverage motion cues, inspired by the common fate principle that pixels that share similar movements tend to belong to the same object.