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.
We show that existing upsampling operators can be unified with the notion of the index function.
We tackle the problem of automatic portrait matting on mobile devices.
We present the first generative adversarial network (GAN) for natural image matting.
In this paper, we first formulate transparent object matting as a refractive flow estimation problem.
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.
Our method employs two encoder networks to extract essential information for matting.