ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2003.03849
  4. Cited By
Active Fine-Tuning from gMAD Examples Improves Blind Image Quality
  Assessment

Active Fine-Tuning from gMAD Examples Improves Blind Image Quality Assessment

8 March 2020
Zhihua Wang
Kede Ma
    AAML
ArXivPDFHTML

Papers citing "Active Fine-Tuning from gMAD Examples Improves Blind Image Quality Assessment"

6 / 6 papers shown
Title
Max360IQ: Blind Omnidirectional Image Quality Assessment with Multi-axis Attention
Max360IQ: Blind Omnidirectional Image Quality Assessment with Multi-axis Attention
Jiebin Yan
Ziwen Tan
Yuming Fang
Jiale Rao
Yifan Zuo
63
1
0
26 Feb 2025
Learning to Blindly Assess Image Quality in the Laboratory and Wild
Learning to Blindly Assess Image Quality in the Laboratory and Wild
Weixia Zhang
Kede Ma
Guangtao Zhai
Xiaokang Yang
29
58
0
01 Jul 2019
NIMA: Neural Image Assessment
NIMA: Neural Image Assessment
Hossein Talebi
P. Milanfar
3DH
49
892
0
15 Sep 2017
Learning a No-Reference Quality Metric for Single-Image Super-Resolution
Learning a No-Reference Quality Metric for Single-Image Super-Resolution
Chao Ma
Chih-Yuan Yang
Xiaokang Yang
Ming-Hsuan Yang
SupR
17
509
0
18 Dec 2016
Deep Neural Networks for No-Reference and Full-Reference Image Quality
  Assessment
Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment
S. Bosse
Dominique Maniry
K. Müller
Thomas Wiegand
Wojciech Samek
49
994
0
06 Dec 2016
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual
  Recognition
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
ObjD
221
11,183
0
18 Jun 2014
1