This paper proposes key instance selection based on video saliency covering
objectness and dynamics for unsupervised video object segmentation (UVOS). Our
method takes frames sequentially and extracts object proposals with
corresponding masks for each frame. We link objects according to their
similarity until the M-th frame and then assign them unique IDs (i.e.,
instances). Similarity measure takes into account multiple properties such as
ReID descriptor, expected trajectory, and semantic co-segmentation result.
After M-th frame, we select K IDs based on video saliency and frequency of
appearance; then only these key IDs are tracked through the remaining frames.
Thanks to these technical contributions, our results are ranked third on the
leaderboard of UVOS DAVIS challenge.

本文提出了一种基于视频显着性的关键实例选择方法，涵盖了对象性和动态特性，用于非监督式视频对象分割，在多个属性如 ReID 描述符，预期轨迹和语义共分割结果等方面考虑相似性度量，并基于视频显着性和出现频率选择关键 ID 进行跟踪。这些技术贡献使得我们在 UVOS DAVIS 挑战的排行榜上排名第三。