Real-time unsupervised video object segmentation by training with images and optical flow with TMO
Real-time unsupervised video object segmentation by training with images and optical flow with TMO
Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation
arXiv paper abstract https://arxiv.org/abs/2309.14786
arXiv PDF paper https://arxiv.org/pdf/2309.14786.pdf
GitHub https://github.com/suhwan-cho/TMO
Unsupervised video object segmentation (VOS) is a task that aims to detect the most salient object in a video without external guidance about the object.
… recent methods … use … optical flow maps … the network is easy to be learned overly dependent on the motion cues during network training.
… design a novel motion-as-option network by treating motion cues as optional.
During network training, RGB images are randomly provided to the motion encoder instead of optical flow maps, to implicitly reduce motion dependency of the network.
As the learned motion encoder can deal with both RGB images and optical flow maps, two different predictions can be generated depending on which source information is used as motion input.
… proposed approach affords state-of-the-art performance on all public benchmark datasets, even maintaining real-time inference speed.
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