Mask Selection and Propagation for Unsupervised Video Object Segmentation

Abstract

In this work we present a novel approach for Unsupervised Video Object Segmentation, that is automatically generating instance level segmentation masks for salient objects and tracking them in a video. We efficiently handle problems present in existing methods such as drift while temporal propagation, tracking and addition of new objects. To this end, we propose a novel idea of improving masks in an online manner using ensemble of criteria whose task is to inspect the quality of masks. We introduce a novel idea of assessing mask quality using a neural network called Selector Net. The proposed network is trained is such way that it is generalizes across various datasets. Our proposed method is able to limit the noise accumulated along the video, giving state of the art result on Davis 2019 Unsupervised challenge dataset with $\mathcal{J}\& {\mathcal{F}}$ mean 61.6%. We also tested on datasets such as FBMS and SegTrack V2 and performed better or on par compared to the other methods.