Segment humans in image after self-supervised training on multiple views
Segment humans in image after self-supervised training on multiple views
Self-supervised Human Detection and Segmentation via Multi-view Consensus
arXiv paper abstract https://arxiv.org/abs/2012.05119
arXiv PDF paper https://arxiv.org/pdf/2012.05119.pdf
Self-supervised detection and segmentation of foreground objects in complex scenes is gaining attention as their fully-supervised counterparts require overly large amounts of annotated data to deliver sufficient accuracy in domain-specific applications.
However, existing self-supervised approaches predominantly rely on restrictive assumptions on appearance and motion, which precludes their use in scenes depicting highly dynamic activities or involve camera motion.
... propose using a multi-camera framework in which geometric constraints are embedded in the form of multi-view consistency during training
... learn a joint distribution of proposals over multiple views.
At inference time, our method operates on single RGB images.
... approach outperforms state-of-the-art self-supervised person detection and segmentation techniques on images that visually depart from those of standard benchmarks, as well as on those of the classical Human3.6M dataset.
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