  Unsupervised Video Object Segmentation (VOS) aims at identifying the contours
of primary foreground objects in videos without any prior knowledge. However,
previous methods do not fully use spatial-temporal context and fail to tackle
this challenging task in real-time. This motivates us to develop an efficient
Long-Short Temporal Attention network (termed LSTA) for unsupervised VOS task
from a holistic view. Specifically, LSTA consists of two dominant modules,
i.e., Long Temporal Memory and Short Temporal Attention. The former captures
the long-term global pixel relations of the past frames and the current frame,
which models constantly present objects by encoding appearance pattern.
Meanwhile, the latter reveals the short-term local pixel relations of one
nearby frame and the current frame, which models moving objects by encoding
motion pattern. To speedup the inference, the efficient projection and the
locality-based sliding window are adopted to achieve nearly linear time
complexity for the two light modules, respectively. Extensive empirical studies
on several benchmarks have demonstrated promising performances of the proposed
method with high efficiency.
