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Yaoyi Li

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2 papers

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AAAI Conference 2020 Conference Paper

Natural Image Matting via Guided Contextual Attention

Yaoyi Li
Hongtao Lu

Over the last few years, deep learning based approaches have achieved outstanding improvements in natural image matting. Many of these methods can generate visually plausible alpha estimations, but typically yield blurry structures or textures in the semitransparent area. This is due to the local ambiguity of transparent objects. One possible solution is to leverage the far-surrounding information to estimate the local opacity. Traditional afﬁnity-based methods often suffer from the high computational complexity, which are not suitable for high resolution alpha estimation. 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. Guided contextual attention module directly propagates high-level opacity information globally based on the learned low-level afﬁnity. The proposed method can mimic information ﬂow of afﬁnity-based methods and utilize rich features learned by deep neural networks simultaneously. Experiment results on Composition-1k testing set and alphamatting. com benchmark dataset demonstrate that our method outperforms state-ofthe-art approaches in natural image matting. Code and models are available at https: //github. com/Yaoyi-Li/GCA-Matting.

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AAAI Conference 2019 Conference Paper

Multi-Attribute Transfer via Disentangled Representation

Jianfu Zhang
Yuanyuan Huang
Yaoyi Li
Weijie Zhao
Liqing Zhang

Recent studies show significant progress in image-to-image translation task, especially facilitated by Generative Adversarial Networks. They can synthesize highly realistic images and alter the attribute labels for the images. However, these works employ attribute vectors to specify the target domain which diminishes image-level attribute diversity. In this paper, we propose a novel model formulating disentangled representations by projecting images to latent units, grouped feature channels of Convolutional Neural Network, to disassemble the information between different attributes. Thanks to disentangled representation, we can transfer attributes according to the attribute labels and moreover retain the diversity beyond the labels, namely, the styles inside each image. This is achieved by specifying some attributes and swapping the corresponding latent units to “swap” the attributes appearance, or applying channel-wise interpolation to blend different attributes. To verify the motivation of our proposed model, we train and evaluate our model on face dataset CelebA. Furthermore, the evaluation of another facial expression dataset RaFD demonstrates the generalizability of our proposed model.

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