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Image Super-Resolution Using Very Deep Residual Channel Attention Networks
Convolutional neural network (CNN) depth is of crucial importance for image super-resolution (SR). However, we observe that deeper networks for image SR are more difficult to train. TheExpand
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Residual Dense Network for Image Super-Resolution
A very deep convolutional neural network (CNN) has recently achieved great success for image super-resolution (SR) and offered hierarchical features as well. However, most deep CNN based SR models doExpand
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Human age estimation using bio-inspired features
We investigate the biologically inspired features (BIF) for human age estimation from faces. As in previous bio-inspired models, a pyramid of Gabor filters are used at all positions of the inputExpand
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Image-Based Human Age Estimation by Manifold Learning and Locally Adjusted Robust Regression
Estimating human age automatically via facial image analysis has lots of potential real-world applications, such as human computer interaction and multimedia communication. However, it is still aExpand
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Learning With $\ell ^{1}$-Graph for Image Analysis
The graph construction procedure essentially determines the potentials of those graph-oriented learning algorithms for image analysis. In this paper, we propose a process to build the so-calledExpand
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Age Synthesis and Estimation via Faces: A Survey
Human age, as an important personal trait, can be directly inferred by distinct patterns emerging from the facial appearance. Derived from rapid advances in computer graphics and machine vision,Expand
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Human Age Estimation With Regression on Discriminative Aging Manifold
  • Yun Fu, T. Huang
  • Computer Science
  • IEEE Transactions on Multimedia
  • 1 June 2008
Recently, extensive studies on human faces in the human-computer interaction (HCI) field reveal significant potentials for designing automatic age estimation systems via face image analysis. TheExpand
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Generalized Transfer Subspace Learning Through Low-Rank Constraint
It is expensive to obtain labeled real-world visual data for use in training of supervised algorithms. Therefore, it is valuable to leverage existing databases of labeled data. However, the data inExpand
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Residual Non-local Attention Networks for Image Restoration
In this paper, we propose a residual non-local attention network for high-quality image restoration. Without considering the uneven distribution of information in the corrupted images, previousExpand
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Tell Me Where to Look: Guided Attention Inference Network
Weakly supervised learning with only coarse labels can obtain visual explanations of deep neural network such as attention maps by back-propagating gradients. These attention maps are then availableExpand
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