Motivation: to learn representations that support reasoning at various levels of visual correspondence from scratch and without human supervision.
Main idea: use cycle-consistency in time as free supervisory signal for learning visual representations from scratch.
At training time, the proposed model learns a feature map representation to be useful for performing cycle-consistent tracking. At test time, they use the acquired representation to find nearest neighbors across space and time.
XFeiF
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Motivation: to learn representations that support reasoning at various levels of visual correspondence from scratch and without human supervision.
Main idea: use cycle-consistency in time as free supervisory signal for learning visual representations from scratch.
At training time, the proposed model learns a feature map representation to be useful for performing cycle-consistent tracking. At test time, they use the acquired representation to find nearest neighbors across space and time.