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Target-Aware Object Discovery and Association for Unsupervised Video Multi-Object Segmentation
[article]
2021
arXiv
pre-print
This paper addresses the task of unsupervised video multi-object segmentation. Current approaches follow a two-stage paradigm: 1) detect object proposals using pre-trained Mask R-CNN, and 2) conduct generic feature matching for temporal association using re-identification techniques. However, the generic features, widely used in both stages, are not reliable for characterizing unseen objects, leading to poor generalization. To address this, we introduce a novel approach for more accurate and
arXiv:2104.04782v1
fatcat:7qa2244w7rgl5ez26euybjcrlq