  We propose a simple, yet powerful approach for unsupervised object
segmentation in videos. We introduce an objective function whose minimum
represents the mask of the main salient object over the input sequence. It only
relies on independent image features and optical flows, which can be obtained
using off-the-shelf self-supervised methods. It scales with the length of the
sequence with no need for superpixels or sparsification, and it generalizes to
different datasets without any specific training. This objective function can
actually be derived from a form of spectral clustering applied to the entire
video. Our method achieves on-par performance with the state of the art on
standard benchmarks (DAVIS2016, SegTrack-v2, FBMS59), while being conceptually
and practically much simpler. Code is available at
https://ponimatkin.github.io/ssl-vos.
