  Learning object segmentation in image and video datasets without human
supervision is a challenging problem. Humans easily identify moving salient
objects in videos using the gestalt principle of common fate, which suggests
that what moves together belongs together. Building upon this idea, we propose
a self-supervised object discovery approach that leverages motion and
appearance information to produce high-quality object segmentation masks.
Specifically, we redesign the traditional graph cut on images to include motion
information in a linear combination with appearance information to produce edge
weights. Remarkably, this step produces object segmentation masks comparable to
the current state-of-the-art on multiple benchmarks. To further improve
performance, we bootstrap a segmentation network trained on these preliminary
masks as pseudo-ground truths to learn from its own outputs via self-training.
We demonstrate the effectiveness of our approach, named LOCATE, on multiple
standard video object segmentation, image saliency detection, and object
segmentation benchmarks, achieving results on par with and, in many cases
surpassing state-of-the-art methods. We also demonstrate the transferability of
our approach to novel domains through a qualitative study on in-the-wild
images. Additionally, we present extensive ablation analysis to support our
design choices and highlight the contribution of each component of our proposed
method.
