  In this paper, we consider the task of unsupervised object discovery in
videos. Previous works have shown promising results via processing optical
flows to segment objects. However, taking flow as input brings about two
drawbacks. First, flow cannot capture sufficient cues when objects remain
static or partially occluded. Second, it is challenging to establish temporal
coherency from flow-only input, due to the missing texture information. To
tackle these limitations, we propose a model for directly processing
consecutive RGB frames, and infer the optical flow between any pair of frames
using a layered representation, with the opacity channels being treated as the
segmentation. Additionally, to enforce object permanence, we apply temporal
consistency loss on the inferred masks from randomly-paired frames, which refer
to the motions at different paces, and encourage the model to segment the
objects even if they may not move at the current time point. Experimentally, we
demonstrate superior performance over previous state-of-the-art methods on
three public video segmentation datasets (DAVIS2016, SegTrackv2, and FBMS-59),
while being computationally efficient by avoiding the overhead of computing
optical flow as input.
