L-CoDe:Language-Based Colorization Using Color-Object Decoupled Conditions

نویسندگان

چکیده

Colorizing a grayscale image is inherently an ill-posed problem with multi-modal uncertainty. Language-based colorization offers natural way of interaction to reduce such uncertainty via user-provided caption. However, the color-object coupling and mismatch issues make mapping from word color difficult. In this paper, we propose L-CoDe, Colorization network using Decoupled conditions. A predictor for object-color corresponding matrix (OCCM) novel attention transfer module (ATM) are introduced solve problem. To deal that results in incorrect correspondence, adopt soft-gated injection (SIM). We further present new dataset containing annotated pairs provide supervisory signals resolving Experimental show our approach outperforms state-of-the-art methods conditioned on captions.

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ژورنال

عنوان ژورنال: Proceedings of the ... AAAI Conference on Artificial Intelligence

سال: 2022

ISSN: ['2159-5399', '2374-3468']

DOI: https://doi.org/10.1609/aaai.v36i3.20170