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A face antispoofing database with diverse attacks
TLDR
A face antispoofing database which covers a diverse range of potential attack variations, and a baseline algorithm is given for comparison, which explores the high frequency information in the facial region to determine the liveness. Expand
Generative Face Completion
TLDR
This paper demonstrates qualitatively and quantitatively that the proposed effective face completion algorithm is able to deal with a large area of missing pixels in arbitrary shapes and generate realistic face completion results. Expand
Multi-objective convolutional learning for face labeling
TLDR
A novel multi-objective learning method that optimizes a single unified deep convolutional network with two distinct non-structured loss functions: one encoding the unary label likelihoods and the other encoding the pairwise label dependencies. Expand
Deep Cascaded Bi-Network for Face Hallucination
TLDR
A novel framework for hallucinating faces of unconstrained poses and with very low resolution, which allows exceptional hallucination quality on in-the-wild low-res faces with significant pose and illumination variations is presented. Expand
Learning Linear Transformations for Fast Image and Video Style Transfer
TLDR
This work presents an approach for universal style transfer that learns the transformation matrix in a data-driven fashion that is efficient yet flexible to transfer different levels of styles with the same auto-encoder network. Expand
Learning Recursive Filters for Low-Level Vision via a Hybrid Neural Network
TLDR
Experimental results show that many low-level vision tasks can be effectively learned and carried out in real-time by the proposed algorithm and is significantly smaller and faster in comparison with a deep CNN based image filter. Expand
Structured Face Hallucination
TLDR
Experimental results demonstrate that the proposed algorithm generates hallucinated face images with favorable quality and adaptability. Expand
Learning Linear Transformations for Fast Arbitrary Style Transfer
TLDR
This work derives the form of transformation matrix theoretically and presents an arbitrary style transfer approach that learns the transformation matrix with a feed-forward network, which is highly efficient yet allows a flexible combination of multi-level styles while preserving content affinity during style transfer process. Expand
Learning Affinity via Spatial Propagation Networks
TLDR
Experiments on the HELEN face parsing and PASCAL VOC-2012 semantic segmentation tasks show that the spatial propagation network provides a general, effective and efficient solution for generating high-quality segmentation results. Expand
SCOPS: Self-Supervised Co-Part Segmentation
TLDR
This work proposes a self-supervised deep learning approach for part segmentation, where several loss functions are devised that aids in predicting part segments that are geometrically concentrated, robust to object variations and are also semantically consistent across different object instances. Expand
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