A Benchmark for Inpainting of Clothing Images with Irregular Holes
- URL: http://arxiv.org/abs/2007.05080v3
- Date: Thu, 27 Aug 2020 17:43:38 GMT
- Title: A Benchmark for Inpainting of Clothing Images with Irregular Holes
- Authors: Furkan K{\i}nl{\i}, Bar{\i}\c{s} \"Ozcan, Furkan K{\i}ra\c{c}
- Abstract summary: We present an extensive benchmark of clothing image inpainting on well-known fashion datasets.
We introduce the use of a dilated version of partial convolutions, which efficiently derive the mask update step.
Experiments show that dilated partial convolutions (DPConv) improve the quantitative inpainting performance when compared to the other inpainting strategies.
- Score: 3.867363075280544
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Fashion image understanding is an active research field with a large number
of practical applications for the industry. Despite its practical impacts on
intelligent fashion analysis systems, clothing image inpainting has not been
extensively examined yet. For that matter, we present an extensive benchmark of
clothing image inpainting on well-known fashion datasets. Furthermore, we
introduce the use of a dilated version of partial convolutions, which
efficiently derive the mask update step, and empirically show that the proposed
method reduces the required number of layers to form fully-transparent masks.
Experiments show that dilated partial convolutions (DPConv) improve the
quantitative inpainting performance when compared to the other inpainting
strategies, especially it performs better when the mask size is 20% or more of
the image. \keywords{image inpainting, fashion image understanding, dilated
convolutions, partial convolutions
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