  Current semi-supervised video object segmentation (VOS) methods usually
leverage the entire features of one frame to predict object masks and update
memory. This introduces significant redundant computations. To reduce
redundancy, we present a Region Aware Video Object Segmentation (RAVOS)
approach that predicts regions of interest (ROIs) for efficient object
segmentation and memory storage. RAVOS includes a fast object motion tracker to
predict their ROIs in the next frame. For efficient segmentation, object
features are extracted according to the ROIs, and an object decoder is designed
for object-level segmentation. For efficient memory storage, we propose motion
path memory to filter out redundant context by memorizing the features within
the motion path of objects between two frames. Besides RAVOS, we also propose a
large-scale dataset, dubbed OVOS, to benchmark the performance of VOS models
under occlusions. Evaluation on DAVIS and YouTube-VOS benchmarks and our new
OVOS dataset show that our method achieves state-of-the-art performance with
significantly faster inference time, e.g., 86.1 J&F at 42 FPS on DAVIS and 84.4
J&F at 23 FPS on YouTube-VOS.
