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Although these learning-based methods have shown impressive performance, they usually fail on the input images that contain multiple objects. The leading cause is that existing models perform learning and colorization on the entire image. In the absence of a clear figure-ground separation, these models cannot effectively locate and learn meaningful object-level semantics. In this paper, we propose a method for achieving instance-aware colorization. Our network architecture leverages an off-the-shelf object detector to obtain cropped object images and uses an instance colorization network to extract object-level features. We use a similar network to extract the full-image features and apply a fusion module to full object-level and image-level features to predict the final colors. Both colorization networks and fusion modules are learned from a large-scale dataset. Experimental results show that our work outperforms existing methods on different quality metrics and achieves state-of-the-art performance on image colorization.","url_abs":"https://arxiv.org/abs/2005.10825v1","url_pdf":"https://arxiv.org/pdf/2005.10825v1.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":"instance-aware-image-colorization","repo_url":"https://github.com/ericsujw/InstColorization","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"instance-aware-image-colorization","repo_url":"https://github.com/ariG23498/instance-aware-colorization-TF","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"colorization","task_name":"Colorization"},{"task_slug":"image-colorization","task_name":"Image Colorization"},{"task_slug":"object","task_name":"Object"},{"task_slug":"point-interactive-image-colorization","task_name":"Point-interactive Image Colorization"}],"methods":[{"method_slug":"colorization","method_name":"Colorization"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/point-interactive-image-colorization-on-cub","task":"Point-interactive Image Colorization","dataset":"CUB-200-2011","model":"InstColor","rank_in_archive_order":2,"of":7,"metrics":{"PSNR@1":"27.69","PSNR@10":"29.45","PSNR@100":"31.45"},"uses_additional_data":true},{"leaderboard":"/sota/point-interactive-image-colorization-on","task":"Point-interactive Image Colorization","dataset":"ImageNet ctest10k","model":"InstColor","rank_in_archive_order":2,"of":4,"metrics":{"PSNR@1":"27.275","PSNR@10":"29.108","PSNR@100":"31.37"},"uses_additional_data":true},{"leaderboard":"/sota/point-interactive-image-colorization-on-1","task":"Point-interactive Image Colorization","dataset":"Oxford 102 Flowers","model":"InstColor","rank_in_archive_order":2,"of":7,"metrics":{"PSNR@1":"22.97","PSNR@10":"25.130","PSNR@100":"27.35"},"uses_additional_data":true}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2005.10825","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.10825"}},"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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