Computer Science
Experimental Result
100%
Face Recognition
61%
super resolution
36%
Feature Extraction
24%
Saliency Detection
23%
Facial Feature
22%
Facial Expression
21%
Convolutional Neural Network
19%
Object Detection
17%
Expression Recognition
16%
Deep Learning Method
16%
Visual Quality
15%
Photometric Stereo
15%
Lighting Condition
15%
Gabor feature
13%
Recognition Rate
13%
local feature
12%
Component Analysis
12%
Principal Components
12%
Hausdorff Distance
10%
Representation Learning
10%
Feature Fusion
10%
Salient Object Detection
10%
Superior Performance
9%
Information Retrieval
9%
face recognition algorithm
9%
facial image
9%
Image Restoration
9%
Detection Algorithm
8%
Attention (Machine Learning)
8%
image denoising
8%
Detection Method
8%
Face Detection
8%
Salient Region
8%
Least Squares Methods
7%
Image Sequence
7%
Single-Image Super Resolution
7%
Image Coding
7%
Activity Recognition
7%
Wavelet Transforms
7%
Art Performance
7%
Feature Map
7%
Training Sample
7%
Efficient Algorithm
7%
Video Sequences
6%
Annotation
6%
Point Cloud
6%
Resolution Image
6%
Feature Selection
6%
Singular Value
6%
Keyphrases
Face Recognition
45%
Face Image
40%
Saliency Detection
27%
Human Face
26%
High-resolution
21%
Facial Features
18%
Image Super-resolution
18%
Face Hallucination
17%
Human Face Recognition
17%
Facial Expression Recognition
16%
State-of-the-art Techniques
16%
Lighting Conditions
14%
Recognition-based
12%
Photometric Stereo
12%
Recognition Rate
12%
Gabor Wavelet
11%
Gabor Features
11%
Face Area
11%
Experiment Results
11%
Object Detection
10%
Transformer
10%
Visual Quality
10%
Saliency Map
10%
Feature-based
10%
Single Image Super-resolution
9%
Facial Image
9%
Convolutional Neural Network
9%
Facial Feature Extraction
9%
High Dynamic Range Imaging
9%
Human Face Image
8%
Face Recognition Algorithm
8%
Remote Sensing Image
8%
Feature Extraction
8%
Superior Performance
8%
Image Denoising
8%
Image Reconstruction
8%
Principal Coordinate Analysis (PCoA)
8%
Color Image
8%
Image Magnification
7%
Deep Learning Methods
7%
Surface Normal
7%
Facial Expression
7%
Training Samples
7%
Super-resolution
7%
Salient Object
7%
Image Sequence
7%
Feature Fusion
7%
Face Database
7%
Local Features
7%
Art Performance
7%
Engineering
Experimental Result
70%
Face Image
37%
Feature Extraction
20%
High Resolution
17%
Similarities
14%
Lighting Condition
13%
Recognition Rate
12%
Multiscale
12%
Convolutional Neural Network
10%
Component Analysis
9%
Principal Components
9%
State-of-the-Art Method
9%
Detection Algorithm
8%
Color Image
8%
Gabor Wavelet
8%
Deep Learning Method
7%
Gaussians
7%
Image Restoration
6%
Boundary Detection
6%
Single Image
6%
Genetic Algorithm
6%
Moving Picture Experts Group
6%
Directional
6%
Reconstructed Image
6%
Joints (Structural Components)
5%
Image Sequence
5%
Feature Vector
5%
Singular Value Decomposition
5%
Low Bit Rate
5%
Natural Image
5%
Feature Point
5%
Computational Complexity
5%
Loss Function
5%
Image Coding
5%
Cost Function
5%
Least Square
5%
Dynamic Range
5%
