Video Object Segmentation (VOS) task aims to segment objects in videos.
However, previous settings either require time-consuming manual masks of target
objects at the first frame during inference or lack the flexibility to specify
arbitrary objects of interest. To address these limitations, we propose the
setting named Click Video Object Segmentation (ClickVOS) which segments objects
of interest across the whole video according to a single click per object in
the first frame. And we provide the extended datasets DAVIS-P and YouTubeVOSP
that with point annotations to support this task. ClickVOS is of significant
practical applications and research implications due to its only 1-2 seconds
interaction time for indicating an object, comparing annotating the mask of an
object needs several minutes. However, ClickVOS also presents increased
challenges. To address this task, we propose an end-to-end baseline approach
named called Attention Before Segmentation (ABS), motivated by the attention
process of humans. ABS utilizes the given point in the first frame to perceive
the target object through a concise yet effective segmentation attention.
Although the initial object mask is possibly inaccurate, in our ABS, as the
video goes on, the initially imprecise object mask can self-heal instead of
deteriorating due to error accumulation, which is attributed to our designed
improvement memory that continuously records stable global object memory and
updates detailed dense memory. In addition, we conduct various baseline
explorations utilizing off-the-shelf algorithms from related fields, which
could provide insights for the further exploration of ClickVOS. The
experimental results demonstrate the superiority of the proposed ABS approach.
Extended datasets and codes will be available at
this https URL

Video Object Segmentation task is addressed by proposing Click Video Object Segmentation (ClickVOS), an end-to-end baseline approach called Attention Before Segmentation (ABS), which utilizes the given point in the first frame to perceive the target object through a concise yet effective segmentation attention, leading to improved object mask accuracy.