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Noisy Annotation Refinement for Object Detection
Jiafeng Mao, Qing Yu, Yoko Yamakata +1
Supervised training of object detectors requires well-annotated large-scale datasets, whose production is costly. Therefore, some efforts have been made to obtain annotations in ec…
NTIRE 2021 Challenge on Perceptual Image Quality Assessment
Jinjin Gu, Haoming Cai, Chao Dong +47
This paper reports on the NTIRE 2021 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (…
A Novel Perspective for Positive-Unlabeled Learning via Noisy Labels
Daiki Tanaka, Daiki Ikami, Kiyoharu Aizawa
Positive-unlabeled learning refers to the process of training a binary classifier using only positive and unlabeled data. Although unlabeled data can contain positive data, all unl…
What If We Only Use Real Datasets for Scene Text Recognition? Toward Scene Text Recognition With Fewer Labels
Jeonghun Baek, Yusuke Matsui, Kiyoharu Aizawa
Scene text recognition (STR) task has a common practice: All state-of-the-art STR models are trained on large synthetic data. In contrast to this practice, training STR models only…