  Video instance segmentation is a complex task in which we need to detect,
segment, and track each object for any given video. Previous approaches only
utilize single-frame features for the detection, segmentation, and tracking of
objects and they suffer in the video scenario due to several distinct
challenges such as motion blur and drastic appearance change. To eliminate
ambiguities introduced by only using single-frame features, we propose a novel
comprehensive feature aggregation approach (CompFeat) to refine features at
both frame-level and object-level with temporal and spatial context
information. The aggregation process is carefully designed with a new attention
mechanism which significantly increases the discriminative power of the learned
features. We further improve the tracking capability of our model through a
siamese design by incorporating both feature similarities and spatial
similarities. Experiments conducted on the YouTube-VIS dataset validate the
effectiveness of proposed CompFeat. Our code will be available at
https://github.com/SHI-Labs/CompFeat-for-Video-Instance-Segmentation.
