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

  title={Active Fine-Tuning From gMAD Examples Improves Blind Image Quality Assessment},
  author={Zhihua Wang and Kede Ma},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  • Zhihua Wang, Kede Ma
  • Published 8 March 2020
  • Computer Science
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
The research in image quality assessment (IQA) has a long history, and significant progress has been made by leveraging recent advances in deep neural networks (DNNs). Despite high correlation numbers on existing IQA datasets, DNN-based models may be easily falsified in the group maximum differentiation (gMAD) competition. Here we show that gMAD examples can be used to improve blind IQA (BIQA) methods. Specifically, we first pre-train a DNN-based BIQA model using multiple noisy annotators, and… 

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