We propose a method for unsupervised video object segmentation by
transferring the knowledge encapsulated in image-based instance embedding
networks. The instance embedding network produces an embedding vector for each
pixel that enables identifying all pixels belonging to the same object. Though
trained on static images, the instance embeddings are stable over consecutive
video frames, which allows us to link objects together over time. Thus, we
adapt the instance networks trained on static images to video object
segmentation and incorporate the embeddings with objectness and optical flow
features, without model retraining or online fine-tuning. The proposed method
outperforms state-of-the-art unsupervised segmentation methods in the DAVIS
dataset and the FBMS dataset.

提出一种用于无监督视频目标分割的方法，通过转移图像实例嵌入网络中封装的知识。通过将实例嵌入与物体性和光流特征相结合，无需模型重新训练或在线微调，即可将物体链接起来。该方法在 DAVIS 数据集和 FBMS 数据集上优于最先进的无监督分割方法。