2022
DOI: 10.1007/978-3-031-19815-1_24
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Don’t Forget Me: Accurate Background Recovery for Text Removal via Modeling Local-Global Context

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Cited by 9 publications
(3 citation statements)
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“…Following previous studies (Liu et al 2020(Liu et al , 2022a, the image-eval metrics include PSNR, MSSIM, MSE, AGE, pEPs, pCEPs, and FID, while the detection-eval metrics involve the precision (P), recall (R), and f-measure (F) using the pretrained CRAFT (Baek et al 2019) for text detection.…”
Section: Evaluation Metricsmentioning
confidence: 99%
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“…Following previous studies (Liu et al 2020(Liu et al , 2022a, the image-eval metrics include PSNR, MSSIM, MSE, AGE, pEPs, pCEPs, and FID, while the detection-eval metrics involve the precision (P), recall (R), and f-measure (F) using the pretrained CRAFT (Baek et al 2019) for text detection.…”
Section: Evaluation Metricsmentioning
confidence: 99%
“…The sequential text localizing and background inpainting pipeline introduces additional parameters, decreases the inference speed, and, more importantly, breaks the integrity of the entire model. The error of text localization can be easily propagated to the background inpainting, especially for the methods that require pre-supplied text detectors (Tang et al 2021;Liu et al 2022a; Lee and Choi 2022). (2) Recent advances (Liu et al 2020;Lyu and Zhu 2022;Du et al 2023b;Wang et al 2023) tend to employ a multi-step paradigm in a coarse-to-fine or progressive fashion, which significantly undermines efficiency and makes it difficult to balance the parameters involved in multiple steps.…”
Section: Introductionmentioning
confidence: 99%
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