Feature-Product networks (FP-nets) are a novel deep-network architecture inspired by principles of biological vision. These networks contain the so-called FP-blocks that learn two different filters for each input feature map, the outputs of which are then multiplied. Such an architecture is inspired by models of end-stopped neurons, which are common in cortical areas V1 and especially in V2. The authors here use FP-nets on three image quality assessment (IQA) benchmarks for blind IQA. They show that by using FP-nets, they can obtain networks that deliver state-of-the-art performance while being significantly more compact than competing models. A further improvement that they obtain is due to a simple attention mechanism. The good results that they report may be related to the fact that they employ bio-inspired design principles.
Original languageEnglish
Title of host publicationHVEI
Publication date2021
Pages10402-1 - 10402-13
Publication statusPublished - 2021

Research Areas and Centers

  • Centers: Center for Artificial Intelligence Luebeck (ZKIL)
  • Research Area: Intelligent Systems

DFG Research Classification Scheme

  • 409-05 Interactive and Intelligent Systems, Image and Language Processing, Computer Graphics and Visualisation


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