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Learning Pixel Trajectories with Multiscale Contrastive Random Walks. (arXiv:2201.08379v1 [cs.CV])
Jan. 21, 2022, 2:10 a.m. | Zhangxing Bian, Allan Jabri, Alexei A. Efros, Andrew Owens
cs.CV updates on arXiv.org arxiv.org
A range of video modeling tasks, from optical flow to multiple object
tracking, share the same fundamental challenge: establishing space-time
correspondence. Yet, approaches that dominate each space differ. We take a step
towards bridging this gap by extending the recent contrastive random walk
formulation to much denser, pixel-level space-time graphs. The main
contribution is introducing hierarchy into the search problem by computing the
transition matrix between two frames in a coarse-to-fine manner, forming a
multiscale contrastive random walk when extended …
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