  In this work we present SwiftNet for real-time semisupervised video object
segmentation (one-shot VOS), which reports 77.8% J &F and 70 FPS on DAVIS 2017
validation dataset, leading all present solutions in overall accuracy and speed
performance. We achieve this by elaborately compressing spatiotemporal
redundancy in matching-based VOS via Pixel-Adaptive Memory (PAM). Temporally,
PAM adaptively triggers memory updates on frames where objects display
noteworthy inter-frame variations. Spatially, PAM selectively performs memory
update and match on dynamic pixels while ignoring the static ones,
significantly reducing redundant computations wasted on segmentation-irrelevant
pixels. To promote efficient reference encoding, light-aggregation encoder is
also introduced in SwiftNet deploying reversed sub-pixel. We hope SwiftNet
could set a strong and efficient baseline for real-time VOS and facilitate its
application in mobile vision. The source code of SwiftNet can be found at
https://github.com/haochenheheda/SwiftNet.
