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Unsupervised Video Object Segmentation (UVOS) aims at discovering objects andtracking them through videos. For accurate UVOS, we observe if one can locateprecise segment proposals on key frames, subsequent processes are much simpler.Hence, we propose to reason about key frame proposals using a graph built withthe object probability masks initially generated from multiple frames aroundthe key frame and then propagated to the key frame. On this graph, we computemaximal cliques, with each clique representing one candidate object. By makingmultiple proposals in the clique to vote for the key frame proposal, we obtainrefined key frame proposals that could be better than any of the single-frameproposals. A semi-supervised VOS algorithm subsequently tracks these key frameproposals to the entire video. Our algorithm is modular and hence can be usedwith any instance segmentation and semi-supervised VOS algorithm. We achievestate-of-the-art performance on the DAVIS-2017 validation and test-dev dataset.On the related problem of video instance segmentation, our method showscompetitive performance with the previous best algorithm that requires jointtraining with the VOS algorithm.

Maximal Cliques on Multi-Frame Proposal Graph for Unsupervised Video Object Segmentation