  Embodied agents must detect and localize objects of interest, e.g. traffic
participants for self-driving cars. Supervision in the form of bounding boxes
for this task is extremely expensive. As such, prior work has looked at
unsupervised instance detection and segmentation, but in the absence of
annotated boxes, it is unclear how pixels must be grouped into objects and
which objects are of interest. This results in over-/under-segmentation and
irrelevant objects. Inspired by human visual system and practical applications,
we posit that the key missing cue for unsupervised detection is motion: objects
of interest are typically mobile objects that frequently move and their motions
can specify separate instances. In this paper, we propose MOD-UV, a Mobile
Object Detector learned from Unlabeled Videos only. We begin with instance
pseudo-labels derived from motion segmentation, but introduce a novel training
paradigm to progressively discover small objects and static-but-mobile objects
that are missed by motion segmentation. As a result, though only learned from
unlabeled videos, MOD-UV can detect and segment mobile objects from a single
static image. Empirically, we achieve state-of-the-art performance in
unsupervised mobile object detection on Waymo Open, nuScenes, and KITTI
Datasets without using any external data or supervised models. Code is
available at https://github.com/YihongSun/MOD-UV.
