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Rather than using\nhand-crafted rules as in traditional exemplar-based methods, our end-to-end\ncolorization network learns how to select, propagate, and predict colors from\nthe large-scale data. The approach performs robustly and generalizes well even\nwhen using reference images that are unrelated to the input grayscale image.\nMore importantly, as opposed to other learning-based colorization methods, our\nnetwork allows the user to achieve customizable results by simply feeding\ndifferent references. In order to further reduce manual effort in selecting the\nreferences, the system automatically recommends references with our proposed\nimage retrieval algorithm, which considers both semantic and luminance\ninformation. The colorization can be performed fully automatically by simply\npicking the top reference suggestion. Our approach is validated through a user\nstudy and favorable quantitative comparisons to the-state-of-the-art methods.\nFurthermore, our approach can be naturally extended to video colorization. Our\ncode and models will be freely available for public use.","url_abs":"http://arxiv.org/abs/1807.06587v2","url_pdf":"http://arxiv.org/pdf/1807.06587v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-exemplar-based-colorization","repo_url":"https://github.com/msracver/Deep-Exemplar-based-Colorization","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"colorization","task_name":"Colorization"},{"task_slug":"image-retrieval","task_name":"Image Retrieval"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[{"method_slug":"colorization","method_name":"Colorization"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.06587","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.06587"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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