Semi-supervised Ranking for Object Image Blur Assessment
release_dplgayndrzhcjndtavtfx2dumq
by
Qiang Li, Zhaoliang Yao, Jingjing Wang, Ye Tian, Pengju Yang, Di Xie, Shiliang Pu
2022
Abstract
Assessing the blurriness of an object image is fundamentally important to
improve the performance for object recognition and retrieval. The main
challenge lies in the lack of abundant images with reliable labels and
effective learning strategies. Current datasets are labeled with limited and
confused quality levels. To overcome this limitation, we propose to label the
rank relationships between pairwise images rather their quality levels, since
it is much easier for humans to label, and establish a large-scale realistic
face image blur assessment dataset with reliable labels. Based on this dataset,
we propose a method to obtain the blur scores only with the pairwise rank
labels as supervision. Moreover, to further improve the performance, we propose
a self-supervised method based on quadruplet ranking consistency to leverage
the unlabeled data more effectively. The supervised and self-supervised methods
constitute a final semi-supervised learning framework, which can be trained
end-to-end. Experimental results demonstrate the effectiveness of our method.
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