59 research outputs found
Mask-guided Style Transfer Network for Purifying Real Images
Recently, the progress of learning-by-synthesis has proposed a training model
for synthetic images, which can effectively reduce the cost of human and
material resources. However, due to the different distribution of synthetic
images compared with real images, the desired performance cannot be achieved.
To solve this problem, the previous method learned a model to improve the
realism of the synthetic images. Different from the previous methods, this
paper try to purify real image by extracting discriminative and robust features
to convert outdoor real images to indoor synthetic images. In this paper, we
first introduce the segmentation masks to construct RGB-mask pairs as inputs,
then we design a mask-guided style transfer network to learn style features
separately from the attention and bkgd(background) regions and learn content
features from full and attention region. Moreover, we propose a novel
region-level task-guided loss to restrain the features learnt from style and
content. Experiments were performed using mixed studies (qualitative and
quantitative) methods to demonstrate the possibility of purifying real images
in complex directions. We evaluate the proposed method on various public
datasets, including LPW, COCO and MPIIGaze. Experimental results show that the
proposed method is effective and achieves the state-of-the-art results.Comment: arXiv admin note: substantial text overlap with arXiv:1903.0582
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