  Unsupervised video object segmentation (UVOS) aims at detecting the primary
objects in a given video sequence without any human interposing. Most existing
methods rely on two-stream architectures that separately encode the appearance
and motion information before fusing them to identify the target and generate
object masks. However, this pipeline is computationally expensive and can lead
to suboptimal performance due to the difficulty of fusing the two modalities
properly. In this paper, we propose a novel UVOS model called SimulFlow that
simultaneously performs feature extraction and target identification, enabling
efficient and effective unsupervised video object segmentation. Concretely, we
design a novel SimulFlow Attention mechanism to bridege the image and motion by
utilizing the flexibility of attention operation, where coarse masks predicted
from fused feature at each stage are used to constrain the attention operation
within the mask area and exclude the impact of noise. Because of the
bidirectional information flow between visual and optical flow features in
SimulFlow Attention, no extra hand-designed fusing module is required and we
only adopt a light decoder to obtain the final prediction. We evaluate our
method on several benchmark datasets and achieve state-of-the-art results. Our
proposed approach not only outperforms existing methods but also addresses the
computational complexity and fusion difficulties caused by two-stream
architectures. Our models achieve 87.4% J & F on DAVIS-16 with the highest
speed (63.7 FPS on a 3090) and the lowest parameters (13.7 M). Our SimulFlow
also obtains competitive results on video salient object detection datasets.
