Putting the Object Back into Video Object Segmentation
This work addresses video object segmentation for computer vision applications, offering a novel method that significantly boosts accuracy and speed, though it is incremental in advancing existing VOS techniques.
The paper tackles the problem of video object segmentation (VOS) by introducing Cutie, a network that uses object-level memory reading to reduce matching noise and improve performance, achieving an 8.7 J&F improvement over XMem and a 4.2 J&F improvement over DeAOT on the MOSE dataset.
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