  Multiple object video object segmentation is a challenging task, specially
for the zero-shot case, when no object mask is given at the initial frame and
the model has to find the objects to be segmented along the sequence. In our
work, we propose a Recurrent network for multiple object Video Object
Segmentation (RVOS) that is fully end-to-end trainable. Our model incorporates
recurrence on two different domains: (i) the spatial, which allows to discover
the different object instances within a frame, and (ii) the temporal, which
allows to keep the coherence of the segmented objects along time. We train RVOS
for zero-shot video object segmentation and are the first ones to report
quantitative results for DAVIS-2017 and YouTube-VOS benchmarks. Further, we
adapt RVOS for one-shot video object segmentation by using the masks obtained
in previous time steps as inputs to be processed by the recurrent module. Our
model reaches comparable results to state-of-the-art techniques in YouTube-VOS
benchmark and outperforms all previous video object segmentation methods not
using online learning in the DAVIS-2017 benchmark. Moreover, our model achieves
faster inference runtimes than previous methods, reaching 44ms/frame on a P100
GPU.
