  Amodal perception requires inferring the full shape of an object that is
partially occluded. This task is particularly challenging on two levels: (1) it
requires more information than what is contained in the instant retina or
imaging sensor, (2) it is difficult to obtain enough well-annotated amodal
labels for supervision. To this end, this paper develops a new framework of
Self-supervised amodal Video object segmentation (SaVos). Our method
efficiently leverages the visual information of video temporal sequences to
infer the amodal mask of objects. The key intuition is that the occluded part
of an object can be explained away if that part is visible in other frames,
possibly deformed as long as the deformation can be reasonably learned.
Accordingly, we derive a novel self-supervised learning paradigm that
efficiently utilizes the visible object parts as the supervision to guide the
training on videos. In addition to learning type prior to complete masks for
known types, SaVos also learns the spatiotemporal prior, which is also useful
for the amodal task and could generalize to unseen types. The proposed
framework achieves the state-of-the-art performance on the synthetic amodal
segmentation benchmark FISHBOWL and the real world benchmark KINS-Video-Car.
Further, it lends itself well to being transferred to novel distributions using
test-time adaptation, outperforming existing models even after the transfer to
a new distribution.
