10 citations · 17 across the 6 of their papers we have counts for
6 papers
NERO: Explainable Out-of-Distribution Detection with Neuron-level Relevance
Anju Chhetri, Jari Korhonen, Prashnna Gyawali +1
Ensuring reliability is paramount in deep learning, particularly within the domain of medical imaging, where diagnostic decisions often hinge on model outputs. The capacity to sepa…
High Resolution Image Quality Database
Huang Huang, Qiang Wan, Jari Korhonen
With technology for digital photography and high resolution displays rapidly evolving and gaining popularity, there is a growing demand for blind image quality assessment (BIQA) mo…
No-Reference Point Cloud Quality Assessment via Weighted Patch Quality Prediction
Jun Cheng, Honglei Su, Jari Korhonen
With the rapid development of 3D vision applications based on point clouds, point cloud quality assessment(PCQA) is becoming an important research topic. However, the prior PCQA me…
Half of an image is enough for quality assessment
Junyong You, Yuan Lin, Jari Korhonen
Deep networks have demonstrated promising results in the field of Image Quality Assessment (IQA). However, there has been limited research on understanding how deep models in IQA w…
Consumer Image Quality Prediction using Recurrent Neural Networks for Spatial Pooling
Jari Korhonen, Yicheng Su, Junyong You
Promising results for subjective image quality prediction have been achieved during the past few years by using convolutional neural networks (CNN). However, the use of CNNs for hi…
Transformer for Image Quality Assessment
Junyong You, Jari Korhonen
Transformer has become the new standard method in natural language processing (NLP), and it also attracts research interests in computer vision area. In this paper we investigate t…