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Optical Flow Training under Limited Label Budget via Active Learning. (arXiv:2203.05053v1 [cs.CV])
March 11, 2022, 2:10 a.m. | Shuai Yuan, Xian Sun, Hannah Kim, Shuzhi Yu, Carlo Tomasi
cs.CV updates on arXiv.org arxiv.org
Supervised training of optical flow predictors generally yields better
accuracy than unsupervised training. However, the improved performance comes at
an often high annotation cost. Semi-supervised training trades off accuracy
against annotation cost. We use a simple yet effective semi-supervised training
method to show that even a small fraction of labels can improve flow accuracy
by a significant margin over unsupervised training. In addition, we propose
active learning methods based on simple heuristics to further reduce the number
of labels required …
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