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To bridge the domain gap to real imagery with no labeling, we train another matting network guided by the first network and by a discriminator that judges the quality of composites.
SOTA for Image Matting on Adobe Matting
By viewing the indices as a function of the feature map, we introduce the concept of "learning to index", and present a novel index-guided encoder-decoder framework where indices are self-learned adaptively from data and are used to guide the downsampling and upsampling stages, without extra training supervision.
SOTA for Scene Segmentation on SUN-RGBD
We show that existing upsampling operators can be unified with the notion of the index function.
#2 best model for Image Matting on Composition-1K
Inspired by affinity-based method and the successes of contextual attention in inpainting, we develop a novel end-to-end approach for natural image matting with a guided contextual attention module, which is specifically designed for image matting.
We tackle the problem of automatic portrait matting on mobile devices.
We present the first generative adversarial network (GAN) for natural image matting.