  We present Cutie, a video object segmentation (VOS) network with object-level
memory reading, which puts the object representation from memory back into the
video object segmentation result. Recent works on VOS employ bottom-up
pixel-level memory reading which struggles due to matching noise, especially in
the presence of distractors, resulting in lower performance in more challenging
data. In contrast, Cutie performs top-down object-level memory reading by
adapting a small set of object queries. Via those, it interacts with the
bottom-up pixel features iteratively with a query-based object transformer (qt,
hence Cutie). The object queries act as a high-level summary of the target
object, while high-resolution feature maps are retained for accurate
segmentation. Together with foreground-background masked attention, Cutie
cleanly separates the semantics of the foreground object from the background.
On the challenging MOSE dataset, Cutie improves by 8.7 J&F over XMem with a
similar running time and improves by 4.2 J&F over DeAOT while being three times
faster. Code is available at: https://hkchengrex.github.io/Cutie
