  In this work, we present a new computer vision task named video object of
interest segmentation (VOIS). Given a video and a target image of interest, our
objective is to simultaneously segment and track all objects in the video that
are relevant to the target image. This problem combines the traditional video
object segmentation task with an additional image indicating the content that
users are concerned with. Since no existing dataset is perfectly suitable for
this new task, we specifically construct a large-scale dataset called
LiveVideos, which contains 2418 pairs of target images and live videos with
instance-level annotations. In addition, we propose a transformer-based method
for this task. We revisit Swin Transformer and design a dual-path structure to
fuse video and image features. Then, a transformer decoder is employed to
generate object proposals for segmentation and tracking from the fused
features. Extensive experiments on LiveVideos dataset show the superiority of
our proposed method.
