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Learning Rich Representations For Structured Visual Prediction Tasks
[article]
2019
arXiv
pre-print
We describe an approach to learning rich representations for images, that enables simple and effective predictors in a range of vision tasks involving spatially structured maps. Our key idea is to map small image elements to feature representations extracted from a sequence of nested regions of increasing spatial extent. These regions are obtained by "zooming out" from the pixel/superpixel all the way to scene-level resolution, and hence we call these zoom-out features. Applied to semantic
arXiv:1908.11820v1
fatcat:n2utrggy5faszodf4noe5ayram