  Semi-supervised video object segmentation (Semi-VOS), which requires only
annotating the first frame of a video to segment future frames, has received
increased attention recently. Among existing pipelines, the
memory-matching-based one is becoming the main research stream, as it can fully
utilize the temporal sequence information to obtain high-quality segmentation
results. Even though this type of method has achieved promising performance,
the overall framework still suffers from heavy computation overhead, mainly
caused by the per-frame dense convolution operations between high-resolution
feature maps and each kernel filter. Therefore, we propose a sparse baseline of
VOS named SpVOS in this work, which develops a novel triple sparse convolution
to reduce the computation costs of the overall VOS framework. The designed
triple gate, taking full consideration of both spatial and temporal redundancy
between adjacent video frames, adaptively makes a triple decision to decide how
to apply the sparse convolution on each pixel to control the computation
overhead of each layer, while maintaining sufficient discrimination capability
to distinguish similar objects and avoid error accumulation. A mixed sparse
training strategy, coupled with a designed objective considering the sparsity
constraint, is also developed to balance the VOS segmentation performance and
computation costs. Experiments are conducted on two mainstream VOS datasets,
including DAVIS and Youtube-VOS. Results show that, the proposed SpVOS achieves
superior performance over other state-of-the-art sparse methods, and even
maintains comparable performance, e.g., an 83.04% (79.29%) overall score on the
DAVIS-2017 (Youtube-VOS) validation set, with the typical non-sparse VOS
baseline (82.88% for DAVIS-2017 and 80.36% for Youtube-VOS) while saving up to
42% FLOPs, showing its application potential for resource-constrained
scenarios.
