{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/colorization/papers/2","list_of":"/task/colorization","task":"Colorization","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":2,"pages_in_order":4,"rows_per_page":100,"rows":[101,200],"of":367,"counts":{"archive_papers_tagged":367,"with_a_code_link":177,"where_syntology_ran_a_sample":47,"not_listed_spam_title":0,"listed":367,"listed_where_code_ran":47,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":40,"every_run_a_failure_of_syntologys_instrument":7,"listed_with_a_run_with_no_instrument_failure":40,"listed_every_run_a_failure_of_syntologys_instrument":7,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/colorization","prev":"/task/colorization","next":"/task/colorization/papers/3","papers":[{"url":"/paper/bigcolor-colorization-using-a-generative","slug":"bigcolor-colorization-using-a-generative","title":"BigColor: Colorization using a Generative Color Prior for Natural Images","date":"2022-07-20","arxiv_id":"2207.09685","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":2,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/bigcolor-colorization-using-a-generative#ran","syntology_url":"https://syntology.ai/paper/2207.09685","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.09685"}},"official":{"repos":["KIMGEONUNG/BigColor"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":2,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/icolorit-towards-propagating-local-hint-to","slug":"icolorit-towards-propagating-local-hint-to","title":"iColoriT: Towards Propagating Local Hint to the Right Region in Interactive Colorization by Leveraging Vision Transformer","date":"2022-07-14","arxiv_id":"2207.06831","repositories_listed":1,"syntology":null},{"url":"/paper/eliminating-gradient-conflict-in-reference","slug":"eliminating-gradient-conflict-in-reference","title":"Eliminating Gradient Conflict in Reference-based Line-Art Colorization","date":"2022-07-13","arxiv_id":"2207.06095","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/eliminating-gradient-conflict-in-reference#ran","syntology_url":"https://syntology.ai/paper/2207.06095","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.06095"}},"official":{"repos":["kunkun0w0/sga"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/styleflow-for-content-fixed-image-to-image","slug":"styleflow-for-content-fixed-image-to-image","title":"StyleFlow For Content-Fixed Image to Image Translation","date":"2022-07-05","arxiv_id":"2207.01909","repositories_listed":1,"syntology":null},{"url":"/paper/polyu-bpcoma-a-dataset-and-benchmark-towards","slug":"polyu-bpcoma-a-dataset-and-benchmark-towards","title":"PolyU-BPCoMa: A Dataset and Benchmark Towards Mobile Colorized Mapping Using a Backpack Multisensorial System","date":"2022-06-15","arxiv_id":"2206.07468","repositories_listed":1,"syntology":null},{"url":"/paper/uvim-a-unified-modeling-approach-for-vision","slug":"uvim-a-unified-modeling-approach-for-vision","title":"UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes","date":"2022-05-20","arxiv_id":"2205.10337","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/uvim-a-unified-modeling-approach-for-vision#ran","syntology_url":"https://syntology.ai/paper/2205.10337","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.10337"}},"official":{"repos":["google-research/big_vision"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/pik-fix-restoring-and-colorizing-old-photo","slug":"pik-fix-restoring-and-colorizing-old-photo","title":"Pik-Fix: Restoring and Colorizing Old Photos","date":"2022-05-04","arxiv_id":"2205.01902","repositories_listed":1,"syntology":null},{"url":"/paper/learning-multi-view-aggregation-in-the-wild","slug":"learning-multi-view-aggregation-in-the-wild","title":"Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation","date":"2022-04-15","arxiv_id":"2204.07548","repositories_listed":1,"syntology":null},{"url":"/paper/bias-in-automated-image-colorization-metrics","slug":"bias-in-automated-image-colorization-metrics","title":"Bias in Automated Image Colorization: Metrics and Error Types","date":"2022-02-16","arxiv_id":"2202.08143","repositories_listed":1,"syntology":null},{"url":"/paper/patch-based-stochastic-attention-for-image","slug":"patch-based-stochastic-attention-for-image","title":"Patch-Based Stochastic Attention for Image Editing","date":"2022-02-07","arxiv_id":"2202.03163","repositories_listed":1,"syntology":null},{"url":"/paper/denoising-diffusion-restoration-models","slug":"denoising-diffusion-restoration-models","title":"Denoising Diffusion Restoration Models","date":"2022-01-27","arxiv_id":"2201.11793","repositories_listed":1,"syntology":null},{"url":"/paper/style-structure-disentangled-features-and","slug":"style-structure-disentangled-features-and","title":"Style-Structure Disentangled Features and Normalizing Flows for Diverse Icon Colorization","date":"2022-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/semantic-sparse-colorization-network-for-deep","slug":"semantic-sparse-colorization-network-for-deep","title":"Semantic-Sparse Colorization Network for Deep Exemplar-based Colorization","date":"2021-12-02","arxiv_id":"2112.01335","repositories_listed":1,"syntology":null},{"url":"/paper/animeceleb-large-scale-animation-celebfaces","slug":"animeceleb-large-scale-animation-celebfaces","title":"AnimeCeleb: Large-Scale Animation CelebHeads Dataset for Head Reenactment","date":"2021-11-15","arxiv_id":"2111.07640","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-learning-disentangled-group","slug":"self-supervised-learning-disentangled-group","title":"Self-Supervised Learning Disentangled Group Representation as Feature","date":"2021-10-28","arxiv_id":"2110.15255","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/self-supervised-learning-disentangled-group#ran","syntology_url":"https://syntology.ai/paper/2110.15255","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.15255"}},"official":{"repos":["Wangt-CN/IP-IRM"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/generative-flows-as-a-general-purpose","slug":"generative-flows-as-a-general-purpose","title":"Generative Flows as a General Purpose Solution for Inverse Problems","date":"2021-10-25","arxiv_id":"2110.13285","repositories_listed":1,"syntology":null},{"url":"/paper/repaint-improving-the-generalization-of-down","slug":"repaint-improving-the-generalization-of-down","title":"Repaint: Improving the Generalization of Down-Stream Visual Tasks by Generating Multiple Instances of Training Examples","date":"2021-10-20","arxiv_id":"2110.10366","repositories_listed":1,"syntology":null},{"url":"/paper/temporally-consistent-video-colorization-with","slug":"temporally-consistent-video-colorization-with","title":"Temporally Consistent Video Colorization with Deep Feature Propagation and Self-regularization Learning","date":"2021-10-09","arxiv_id":"2110.04562","repositories_listed":1,"syntology":null},{"url":"/paper/towards-vivid-and-diverse-image-colorization","slug":"towards-vivid-and-diverse-image-colorization","title":"Towards Vivid and Diverse Image Colorization with Generative Color Prior","date":"2021-08-19","arxiv_id":"2108.08826","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/towards-vivid-and-diverse-image-colorization#ran","syntology_url":"https://syntology.ai/paper/2108.08826","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.08826"}},"official":{"repos":["ToTheBeginning/GCP-Colorization"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/focusing-on-persons-colorizing-old-images","slug":"focusing-on-persons-colorizing-old-images","title":"Focusing on Persons: Colorizing Old Images Learning from Modern Historical Movies","date":"2021-08-14","arxiv_id":"2108.06515","repositories_listed":1,"syntology":null},{"url":"/paper/ukiyo-e-analysis-and-creativity-with","slug":"ukiyo-e-analysis-and-creativity-with","title":"Ukiyo-e Analysis and Creativity with Attribute and Geometry Annotation","date":"2021-06-04","arxiv_id":"2106.02267","repositories_listed":1,"syntology":null},{"url":"/paper/beyond-the-spectrum-detecting-deepfakes-via","slug":"beyond-the-spectrum-detecting-deepfakes-via","title":"Beyond the Spectrum: Detecting Deepfakes via Re-Synthesis","date":"2021-05-29","arxiv_id":"2105.14376","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":3,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/beyond-the-spectrum-detecting-deepfakes-via#ran","syntology_url":"https://syntology.ai/paper/2105.14376","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.14376"}},"official":{"repos":["SSAW14/BeyondtheSpectrum"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/conditional-invertible-neural-networks-for","slug":"conditional-invertible-neural-networks-for","title":"Conditional Invertible Neural Networks for Diverse Image-to-Image Translation","date":"2021-05-05","arxiv_id":"2105.02104","repositories_listed":1,"syntology":null},{"url":"/paper/thermal-infrared-image-colorization-for","slug":"thermal-infrared-image-colorization-for","title":"Thermal Infrared Image Colorization for Nighttime Driving Scenes with Top-Down Guided Attention","date":"2021-04-29","arxiv_id":"2104.14374","repositories_listed":1,"syntology":null},{"url":"/paper/vcgan-video-colorization-with-hybrid","slug":"vcgan-video-colorization-with-hybrid","title":"VCGAN: Video Colorization with Hybrid Generative Adversarial Network","date":"2021-04-26","arxiv_id":"2104.12357","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":6,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":3,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/vcgan-video-colorization-with-hybrid#ran","syntology_url":"https://syntology.ai/paper/2104.12357","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.12357"}},"official":{"repos":["zhaoyuzhi/VCGAN"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/adversarial-segmentation-loss-for-sketch","slug":"adversarial-segmentation-loss-for-sketch","title":"Adversarial Segmentation Loss for Sketch Colorization","date":"2021-02-11","arxiv_id":"2102.06192","repositories_listed":1,"syntology":null},{"url":"/paper/culture-inspired-multi-modal-color-palette","slug":"culture-inspired-multi-modal-color-palette","title":"Culture-inspired Multi-modal Color Palette Generation and Colorization: A Chinese Youth Subculture Case","date":"2021-02-10","arxiv_id":"2102.05231","repositories_listed":1,"syntology":null},{"url":"/paper/time-travel-rephotography","slug":"time-travel-rephotography","title":"Time-Travel Rephotography","date":"2020-12-22","arxiv_id":"2012.12261","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/time-travel-rephotography#ran","syntology_url":"https://syntology.ai/paper/2012.12261","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.12261"}},"official":{"repos":["Time-Travel-Rephotography/Time-Travel-Rephotography.github.io"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/legacy-photo-editing-with-learned-noise-prior","slug":"legacy-photo-editing-with-learned-noise-prior","title":"Legacy Photo Editing with Learned Noise Prior","date":"2020-11-23","arxiv_id":"2011.11309","repositories_listed":1,"syntology":null},{"url":"/paper/scgan-saliency-map-guided-colorization-with","slug":"scgan-saliency-map-guided-colorization-with","title":"SCGAN: Saliency Map-guided Colorization with Generative Adversarial Network","date":"2020-11-23","arxiv_id":"2011.11377","repositories_listed":1,"syntology":null},{"url":"/paper/procedural-3d-terrain-generation-using","slug":"procedural-3d-terrain-generation-using","title":"Procedural 3D Terrain Generation using Generative Adversarial Networks","date":"2020-10-13","arxiv_id":"2010.06411","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-image-to-image-translation-via","slug":"unsupervised-image-to-image-translation-via","title":"Unsupervised Image-to-Image Translation via Pre-trained StyleGAN2 Network","date":"2020-10-12","arxiv_id":"2010.05713","repositories_listed":1,"syntology":null},{"url":"/paper/learning-more-expressive-joint-distributions","slug":"learning-more-expressive-joint-distributions","title":"Learning more expressive joint distributions in multimodal variational methods","date":"2020-09-08","arxiv_id":"2009.03651","repositories_listed":1,"syntology":null},{"url":"/paper/image-colorization-a-survey-and-dataset","slug":"image-colorization-a-survey-and-dataset","title":"Image Colorization: A Survey and Dataset","date":"2020-08-25","arxiv_id":"2008.10774","repositories_listed":1,"syntology":null},{"url":"/paper/colorization-of-depth-map-via-disentanglement","slug":"colorization-of-depth-map-via-disentanglement","title":"Colorization of Depth Map via Disentanglement","date":"2020-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/transformation-consistency-regularization-a","slug":"transformation-consistency-regularization-a","title":"Transformation Consistency Regularization- A Semi-Supervised Paradigm for Image-to-Image Translation","date":"2020-07-15","arxiv_id":"2007.07867","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-cnn-based-pansharpening-guided","slug":"rethinking-cnn-based-pansharpening-guided","title":"Rethinking CNN-Based Pansharpening: Guided Colorization of Panchromatic Images via GANs","date":"2020-06-30","arxiv_id":"2006.16644","repositories_listed":1,"syntology":null},{"url":"/paper/the-color-out-of-space-learning-self","slug":"the-color-out-of-space-learning-self","title":"The color out of space: learning self-supervised representations for Earth Observation imagery","date":"2020-06-22","arxiv_id":"2006.12119","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/the-color-out-of-space-learning-self#ran","syntology_url":"https://syntology.ai/paper/2006.12119","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.12119"}},"official":{"repos":["stevinc/TheColorOutOfSpace"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/semantic-driven-colorization","slug":"semantic-driven-colorization","title":"Semantic-driven Colorization","date":"2020-06-13","arxiv_id":"2006.07587","repositories_listed":1,"syntology":null},{"url":"/paper/stylization-based-architecture-for-fast-deep","slug":"stylization-based-architecture-for-fast-deep","title":"Stylization-Based Architecture for Fast Deep Exemplar Colorization","date":"2020-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/bridging-the-gap-between-natural-and-medical","slug":"bridging-the-gap-between-natural-and-medical","title":"Bridging the gap between Natural and Medical Images through Deep Colorization","date":"2020-05-21","arxiv_id":"2005.10589","repositories_listed":1,"syntology":null},{"url":"/paper/deephist-differentiable-joint-and-color","slug":"deephist-differentiable-joint-and-color","title":"DeepHist: Differentiable Joint and Color Histogram Layers for Image-to-Image Translation","date":"2020-05-06","arxiv_id":"2005.03995","repositories_listed":1,"syntology":null},{"url":"/paper/gimp-ml-python-plugins-for-using-computer","slug":"gimp-ml-python-plugins-for-using-computer","title":"GIMP-ML: Python Plugins for using Computer Vision Models in GIMP","date":"2020-04-27","arxiv_id":"2004.13060","repositories_listed":1,"syntology":null},{"url":"/paper/cycle-cnn-for-colorization-towards-real","slug":"cycle-cnn-for-colorization-towards-real","title":"Cycle-CNN for Colorization towards Real Monochrome-Color Camera Systems","date":"2020-04-03","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/towards-lifelong-self-supervision-for","slug":"towards-lifelong-self-supervision-for","title":"Towards Lifelong Self-Supervision For Unpaired Image-to-Image Translation","date":"2020-03-31","arxiv_id":"2004.00161","repositories_listed":1,"syntology":null},{"url":"/paper/bilingunet-image-segmentation-by-modulating","slug":"bilingunet-image-segmentation-by-modulating","title":"Modulating Bottom-Up and Top-Down Visual Processing via Language-Conditional Filters","date":"2020-03-28","arxiv_id":"2003.12739","repositories_listed":1,"syntology":null},{"url":"/paper/videoonenet-bidirectional-convolutional","slug":"videoonenet-bidirectional-convolutional","title":"VideoOneNet: Bidirectional Convolutional Recurrent OneNet with Trainable Data Steps for Video Processing","date":"2020-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/nanoscale-microscopy-images-colourisation","slug":"nanoscale-microscopy-images-colourisation","title":"Nanoscale Microscopy Images Colorization Using Neural Networks","date":"2019-12-17","arxiv_id":"1912.07964","repositories_listed":1,"syntology":null},{"url":"/paper/image-processing-using-multi-code-gan-prior","slug":"image-processing-using-multi-code-gan-prior","title":"Image Processing Using Multi-Code GAN Prior","date":"2019-12-15","arxiv_id":"1912.07116","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-colorization-of-icons-based-on","slug":"adversarial-colorization-of-icons-based-on","title":"Adversarial Colorization Of Icons Based On Structure And Color Conditions","date":"2019-10-03","arxiv_id":"1910.05253","repositories_listed":1,"syntology":null},{"url":"/paper/an-unpaired-sketch-to-photo-translation-model","slug":"an-unpaired-sketch-to-photo-translation-model","title":"Unsupervised Sketch-to-Photo Synthesis","date":"2019-09-18","arxiv_id":"1909.08313","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":6,"n_pointer_only":3,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 1 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/an-unpaired-sketch-to-photo-translation-model#ran","syntology_url":"https://syntology.ai/paper/1909.08313","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.08313"}},"official":null}},{"url":"/paper/deep-exemplar-based-video-colorization-1","slug":"deep-exemplar-based-video-colorization-1","title":"Deep Exemplar-based Video Colorization","date":"2019-06-24","arxiv_id":"1906.09909","repositories_listed":1,"syntology":{"n":15,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":11,"n_pointer_only":1,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 1 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/deep-exemplar-based-video-colorization-1#ran","syntology_url":"https://syntology.ai/paper/1906.09909","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.09909"}},"official":{"repos":["zhangmozhe/video-colorization"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/coloring-with-limited-data-few-shot-1","slug":"coloring-with-limited-data-few-shot-1","title":"Coloring With Limited Data: Few-Shot Colorization via Memory-Augmented Networks","date":"2019-06-09","arxiv_id":"1906.11888","repositories_listed":1,"syntology":null},{"url":"/paper/side-window-filtering","slug":"side-window-filtering","title":"Side Window Filtering","date":"2019-05-17","arxiv_id":"1905.07177","repositories_listed":1,"syntology":null},{"url":"/paper/big-but-imperceptible-adversarial","slug":"big-but-imperceptible-adversarial","title":"Unrestricted Adversarial Examples via Semantic Manipulation","date":"2019-04-12","arxiv_id":"1904.06347","repositories_listed":1,"syntology":null},{"url":"/paper/sampling-using-neural-networks-for-colorizing","slug":"sampling-using-neural-networks-for-colorizing","title":"Sampling Using Neural Networks for colorizing the grayscale images","date":"2018-12-27","arxiv_id":"1812.10650","repositories_listed":1,"syntology":null},{"url":"/paper/learning-blind-video-temporal-consistency","slug":"learning-blind-video-temporal-consistency","title":"Learning Blind Video Temporal Consistency","date":"2018-08-01","arxiv_id":"1808.00449","repositories_listed":1,"syntology":null},{"url":"/paper/deep-exemplar-based-colorization","slug":"deep-exemplar-based-colorization","title":"Deep Exemplar-based Colorization","date":"2018-07-17","arxiv_id":"1807.06587","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/deep-exemplar-based-colorization#ran","syntology_url":"https://syntology.ai/paper/1807.06587","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.06587"}},"official":{"repos":["msracver/Deep-Exemplar-based-Colorization"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/tracking-emerges-by-colorizing-videos","slug":"tracking-emerges-by-colorizing-videos","title":"Tracking Emerges by Colorizing Videos","date":"2018-06-25","arxiv_id":"1806.09594","repositories_listed":1,"syntology":null},{"url":"/paper/latent-convolutional-models","slug":"latent-convolutional-models","title":"Latent Convolutional Models","date":"2018-06-16","arxiv_id":"1806.06284","repositories_listed":1,"syntology":null},{"url":"/paper/learning-on-the-edge-explicit-boundary","slug":"learning-on-the-edge-explicit-boundary","title":"Learning on the Edge: Explicit Boundary Handling in CNNs","date":"2018-05-08","arxiv_id":"1805.03106","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-color-from-language","slug":"learning-to-color-from-language","title":"Learning to Color from Language","date":"2018-04-17","arxiv_id":"1804.06026","repositories_listed":1,"syntology":null},{"url":"/paper/coloring-with-words-guiding-image","slug":"coloring-with-words-guiding-image","title":"Coloring with Words: Guiding Image Colorization Through Text-based Palette Generation","date":"2018-04-11","arxiv_id":"1804.04128","repositories_listed":1,"syntology":null},{"url":"/paper/mix-and-match-networks-encoder-decoder","slug":"mix-and-match-networks-encoder-decoder","title":"Mix and match networks: encoder-decoder alignment for zero-pair image translation","date":"2018-04-06","arxiv_id":"1804.02199","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-diversity-of-realistic-image-synthesis","slug":"on-the-diversity-of-realistic-image-synthesis","title":"On the Diversity of Realistic Image Synthesis","date":"2017-12-20","arxiv_id":"1712.07329","repositories_listed":1,"syntology":null},{"url":"/paper/improving-video-generation-for-multi","slug":"improving-video-generation-for-multi","title":"Improving Video Generation for Multi-functional Applications","date":"2017-11-30","arxiv_id":"1711.11453","repositories_listed":1,"syntology":null},{"url":"/paper/language-based-image-editing-with-recurrent","slug":"language-based-image-editing-with-recurrent","title":"Language-Based Image Editing with Recurrent Attentive Models","date":"2017-11-16","arxiv_id":"1711.06288","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/language-based-image-editing-with-recurrent#ran","syntology_url":"https://syntology.ai/paper/1711.06288","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.06288"}},"official":{"repos":["Jianbo-Lab/LBIE"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/cgan-based-manga-colorization-using-a-single","slug":"cgan-based-manga-colorization-using-a-single","title":"cGAN-based Manga Colorization Using a Single Training Image","date":"2017-06-21","arxiv_id":"1706.06918","repositories_listed":1,"syntology":null},{"url":"/paper/comicolorization-semi-automatic-manga","slug":"comicolorization-semi-automatic-manga","title":"Comicolorization: Semi-Automatic Manga Colorization","date":"2017-06-21","arxiv_id":"1706.06759","repositories_listed":1,"syntology":null},{"url":"/paper/probabilistic-image-colorization","slug":"probabilistic-image-colorization","title":"Probabilistic Image Colorization","date":"2017-05-11","arxiv_id":"1705.04258","repositories_listed":1,"syntology":null},{"url":"/paper/colorization-as-a-proxy-task-for-visual","slug":"colorization-as-a-proxy-task-for-visual","title":"Colorization as a Proxy Task for Visual Understanding","date":"2017-03-11","arxiv_id":"1703.04044","repositories_listed":1,"syntology":{"n":10,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/colorization-as-a-proxy-task-for-visual#ran","syntology_url":"https://syntology.ai/paper/1703.04044","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1703.04044"}},"official":{"repos":["gustavla/self-supervision"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/unsupervised-diverse-colorization-via","slug":"unsupervised-diverse-colorization-via","title":"Unsupervised Diverse Colorization via Generative Adversarial Networks","date":"2017-02-22","arxiv_id":"1702.06674","repositories_listed":1,"syntology":null},{"url":"/paper/generalized-deep-image-to-image-regression","slug":"generalized-deep-image-to-image-regression","title":"Generalized Deep Image to Image Regression","date":"2016-12-10","arxiv_id":"1612.03268","repositories_listed":1,"syntology":null},{"url":"/paper/learning-diverse-image-colorization","slug":"learning-diverse-image-colorization","title":"Learning Diverse Image Colorization","date":"2016-12-06","arxiv_id":"1612.01958","repositories_listed":1,"syntology":null},{"url":"/paper/scribbler-controlling-deep-image-synthesis","slug":"scribbler-controlling-deep-image-synthesis","title":"Scribbler: Controlling Deep Image Synthesis with Sketch and Color","date":"2016-12-02","arxiv_id":"1612.00835","repositories_listed":1,"syntology":null},{"url":"/paper/deep-colorization","slug":"deep-colorization","title":"Deep Colorization","date":"2016-04-30","arxiv_id":"1605.00075","repositories_listed":1,"syntology":null},{"url":"/paper/regularized-discrete-optimal-transport","slug":"regularized-discrete-optimal-transport","title":"Regularized Discrete Optimal Transport","date":"2013-07-21","arxiv_id":"1307.5551","repositories_listed":1,"syntology":null},{"url":null,"slug":"mtsic-multi-stage-transformer-based-gan-for","title":"MTSIC: Multi-stage Transformer-based GAN for Spectral Infrared Image Colorization","date":"2025-06-21","arxiv_id":"2506.17540","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-feature-extraction-for","title":"Self-supervised Feature Extraction for Enhanced Ball Detection on Soccer Robots","date":"2025-06-20","arxiv_id":"2506.16821","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-the-exact-denoising-posterior","title":"Exploiting the Exact Denoising Posterior Score in Training-Free Guidance of Diffusion Models","date":"2025-06-16","arxiv_id":"2506.13614","repositories_listed":0,"syntology":null},{"url":null,"slug":"restoring-real-world-images-with-an-internal","title":"Restoring Real-World Images with an Internal Detail Enhancement Diffusion Model","date":"2025-05-24","arxiv_id":"2505.18674","repositories_listed":0,"syntology":null},{"url":null,"slug":"controllable-image-colorization-with-instance","title":"Controllable Image Colorization with Instance-aware Texts and Masks","date":"2025-05-13","arxiv_id":"2505.08705","repositories_listed":0,"syntology":null},{"url":null,"slug":"colorvein-colorful-cancelable-vein-biometrics","title":"ColorVein: Colorful Cancelable Vein Biometrics","date":"2025-04-19","arxiv_id":"2504.14253","repositories_listed":0,"syntology":null},{"url":null,"slug":"cobra-efficient-line-art-colorization-with","title":"Cobra: Efficient Line Art COlorization with BRoAder References","date":"2025-04-16","arxiv_id":"2504.12240","repositories_listed":0,"syntology":null},{"url":null,"slug":"tracktention-leveraging-point-tracking-to","title":"Tracktention: Leveraging Point Tracking to Attend Videos Faster and Better","date":"2025-03-25","arxiv_id":"2503.19904","repositories_listed":0,"syntology":null},{"url":null,"slug":"magiccolor-multi-instance-sketch-colorization","title":"MagicColor: Multi-Instance Sketch Colorization","date":"2025-03-21","arxiv_id":"2503.16948","repositories_listed":0,"syntology":null},{"url":null,"slug":"superpc-a-single-diffusion-model-for-point","title":"SuperPC: A Single Diffusion Model for Point Cloud Completion, Upsampling, Denoising, and Colorization","date":"2025-03-18","arxiv_id":"2503.14558","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-jgac-end-to-end-deep-joint-geometry-and","title":"Deep-JGAC: End-to-End Deep Joint Geometry and Attribute Compression for Dense Colored Point Clouds","date":"2025-02-25","arxiv_id":"2502.17939","repositories_listed":0,"syntology":null},{"url":null,"slug":"novel-computational-workflows-for-natural-and","title":"Novel computational workflows for natural and biomedical image processing based on hypercomplex algebras","date":"2025-02-11","arxiv_id":"2502.07758","repositories_listed":0,"syntology":null},{"url":null,"slug":"overcoming-fake-solutions-in-semi-dual-neural","title":"Overcoming Fake Solutions in Semi-Dual Neural Optimal Transport: A Smoothing Approach for Learning the Optimal Transport Plan","date":"2025-02-07","arxiv_id":"2502.04583","repositories_listed":0,"syntology":null},{"url":null,"slug":"proxy-prompt-endowing-sam-and-sam-2-with-auto","title":"Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation","date":"2025-02-05","arxiv_id":"2502.03501","repositories_listed":0,"syntology":null},{"url":null,"slug":"consistent-video-colorization-via-palette","title":"Consistent Video Colorization via Palette Guidance","date":"2025-01-31","arxiv_id":"2501.19331","repositories_listed":0,"syntology":null},{"url":null,"slug":"vangogh-a-unified-multimodal-diffusion-based","title":"VanGogh: A Unified Multimodal Diffusion-based Framework for Video Colorization","date":"2025-01-16","arxiv_id":"2501.09499","repositories_listed":0,"syntology":null},{"url":null,"slug":"manganinja-line-art-colorization-with-precise","title":"MangaNinja: Line Art Colorization with Precise Reference Following","date":"2025-01-14","arxiv_id":"2501.08332","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-cross-view-correspondence","title":"Self-Supervised Cross-View Correspondence with Predictive Cycle Consistency","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"anidoc-animation-creation-made-easier","title":"AniDoc: Animation Creation Made Easier","date":"2024-12-18","arxiv_id":"2412.14173","repositories_listed":0,"syntology":null},{"url":null,"slug":"enabling-region-specific-control-via-lassos","title":"Enabling Region-Specific Control via Lassos in Point-Based Colorization","date":"2024-12-18","arxiv_id":"2412.13469","repositories_listed":0,"syntology":null},{"url":null,"slug":"colorflow-retrieval-augmented-image-sequence","title":"ColorFlow: Retrieval-Augmented Image Sequence Colorization","date":"2024-12-16","arxiv_id":"2412.11815","repositories_listed":0,"syntology":null},{"url":null,"slug":"structurally-consistent-mri-colorization","title":"Structurally Consistent MRI Colorization using Cross-modal Fusion Learning","date":"2024-12-12","arxiv_id":"2412.10452","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-spatially-decoupled-color","title":"Learning Spatially Decoupled Color Representations for Facial Image Colorization","date":"2024-12-10","arxiv_id":"2412.07203","repositories_listed":0,"syntology":null}],"record_sha256":"a6c061a51a5d5f58842f37d7ac6af1b9bce119297b7efd2434466dbc2a5bb0e9","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}