Weakly-Supervised Spatial Context Networks

10 Apr 2017 Zuxuan Wu Larry S. Davis Leonid Sigal

We explore the power of spatial context as a self-supervisory signal for learning visual representations. In particular, we propose spatial context networks that learn to predict a representation of one image patch from another image patch, within the same image, conditioned on their real-valued relative spatial offset... (read more)

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