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View Scopus Profile
Go Irie
Associate Professor
Faculty of Engineering
,
Department of Information and Computer Technology
Website
https://www.tus.ac.jp/ridai/doc/ji/RIJIA01Detail.php?act=nam&kin=ken&diu=7560&pri=en
h-index
1036
Citations
16
h-index
Calculated based on number of publications stored in Pure and citations from Scopus
2008
2025
Research activity per year
Overview
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Network
Research output
(75)
Similar Profiles
(2)
Fingerprint
Dive into the research topics where Go Irie is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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Weight
Alphabetically
Computer Science
Image Retrieval
100%
Information Retrieval
93%
Experimental Result
69%
Domain Adaptation
66%
Visual Feature
63%
Pose Estimation
58%
retrieval performance
50%
Event Detection
50%
Depth Estimation
50%
Hashing
50%
Data Augmentation
50%
Prediction Accuracy
46%
Semantic Image
43%
Object Recognition
42%
Image Matching
41%
Training Sample
39%
image feature
38%
Behavior Model
33%
Forward Algorithm
33%
color histogram
33%
Computational Modeling
33%
Process Approach
33%
Affective Information
33%
Feature Individual
33%
Video Collection
33%
Dimensional Image
33%
Scale-Invariant Feature Transform
33%
Multimodal Learning
33%
Detection Method
33%
Class Distribution
33%
Estimation Accuracy
31%
Image Processing
30%
Annotation
30%
Reconstruction Error
28%
User Preference
27%
Network Segmentation
27%
Unsupervised Domain Adaptation
27%
Correlation Analysis
27%
Contrastive Learning
27%
Clustering Graph
27%
Unsupervised Learning
27%
Convolutional Neural Network
27%
Optimization Problem
26%
Semisupervised Learning
25%
Spectral Clustering
25%
Propagation Algorithm
25%
Object Detection
25%
Emotion Category
25%
Hash Code
22%
Quantisation Error
22%
Keyphrases
Image Retrieval
61%
TRECVID
50%
Retrieval Practice
45%
Semantic Image Retrieval
44%
Unsupervised Learning
44%
Object Recognition
40%
Content-based Recommendation
38%
Pose Estimation
38%
Object Pose
38%
Image Matching
37%
Affective Content
37%
Unsupervised Domain Adaptation
36%
Domain Adaptation
36%
Popular
33%
Hamming Space
33%
Geometry Preserving
33%
Multimodal Learning
33%
Color Histogram
33%
Forward-backward Algorithm
33%
Video Materials
33%
Variable Speed
33%
Frame Removal
33%
Cut Detection
33%
Junk
33%
Audio-visual Event Detection
33%
Cross-modal Hashing
33%
Subspace Clustering
33%
Data Augmentation
33%
Video Browsing
33%
Open-set Domain Adaptation
33%
Self-labeling
33%
Semantic Content
33%
Triplet Learning
33%
Histogram Features
33%
Image Registration
33%
Binary Codes
33%
Deep Reinforcement
33%
Audio
33%
Travel Route Recommendation
33%
Local Clustering
33%
Video Collection
33%
Photographer
33%
Driving Model
33%
Scene Classification
33%
Affective Scenes
33%
Latent Topics
33%
Monocular Depth Estimation
33%
DP Matching
33%
Target Domain
32%
Estimation Accuracy
31%
