{"id":"https://openalex.org/W7133341617","doi":"https://doi.org/10.48550/arxiv.2603.01893","title":"Generative Visual Chain-of-Thought for Image Editing","display_name":"Generative Visual Chain-of-Thought for Image Editing","publication_year":2026,"publication_date":"2026-03-02","ids":{"openalex":"https://openalex.org/W7133341617","doi":"https://doi.org/10.48550/arxiv.2603.01893"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.01893","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01893","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.01893","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5122678908","display_name":"Zijin Yin","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yin, Zijin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128032304","display_name":"Tiankai Hang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hang, Tiankai","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127997296","display_name":"Yiji Cheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Yiji","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128012441","display_name":"Shiyi Zhang (1445722)","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Shiyi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101275668","display_name":"Runze He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Runze","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127932463","display_name":"Yu Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Yu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128015375","display_name":"Chunyu Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Chunyu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127876408","display_name":"Bing Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Bing","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128026035","display_name":"Zheng Chang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chang, Zheng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128020157","display_name":"Kongming Liang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang, Kongming","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128013301","display_name":"Qinglin Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Qinglin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5127894527","display_name":"Zhanyu Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Zhanyu","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":12,"corresponding_author_ids":["https://openalex.org/A5122678908"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.5242999792098999,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.5242999792098999,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.23479999601840973,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":0.04149999842047691,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/visual-reasoning","display_name":"Visual reasoning","score":0.5960000157356262},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5954999923706055},{"id":"https://openalex.org/keywords/trace","display_name":"TRACE (psycholinguistics)","score":0.5077999830245972},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.5049999952316284},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4973999857902527},{"id":"https://openalex.org/keywords/image-editing","display_name":"Image editing","score":0.4966000020503998},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.43479999899864197},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4251999855041504}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8122000098228455},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6560999751091003},{"id":"https://openalex.org/C2777508537","wikidata":"https://www.wikidata.org/wiki/Q7936620","display_name":"Visual reasoning","level":2,"score":0.5960000157356262},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5954999923706055},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.5077999830245972},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.5049999952316284},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4973999857902527},{"id":"https://openalex.org/C2776674983","wikidata":"https://www.wikidata.org/wiki/Q545981","display_name":"Image editing","level":3,"score":0.4966000020503998},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.43479999899864197},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4251999855041504},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.3255000114440918},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.3165000081062317},{"id":"https://openalex.org/C2779321571","wikidata":"https://www.wikidata.org/wiki/Q7936605","display_name":"Visual learning","level":2,"score":0.3100000023841858},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3093999922275543},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.29420000314712524},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.27959999442100525},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.27390000224113464},{"id":"https://openalex.org/C111370547","wikidata":"https://www.wikidata.org/wiki/Q7451120","display_name":"Sensory cue","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.2574999928474426},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.25699999928474426},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2565000057220459},{"id":"https://openalex.org/C155911833","wikidata":"https://www.wikidata.org/wiki/Q3817354","display_name":"Spatial intelligence","level":2,"score":0.25609999895095825}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.01893","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01893","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.01893","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01893","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Existing":[0],"image":[1,201],"editing":[2,70,106,156],"methods":[3],"struggle":[4],"to":[5,8,41,113,136,151,166],"perceive":[6],"where":[7],"edit,":[9],"especially":[10],"under":[11,170],"complex":[12],"scenes":[13,172],"and":[14,46,69,86,155,173,187,199],"nuanced":[15],"spatial":[16,39,83],"instructions.":[17],"To":[18],"address":[19],"this":[20,114],"issue,":[21],"we":[22,116,159],"propose":[23],"Generative":[24],"Visual":[25],"Chain-of-Thought":[26],"(GVCoT),":[27],"a":[28,119,130,162],"unified":[29],"framework":[30],"that":[31,179],"performs":[32],"native":[33],"visual":[34,57,63],"reasoning":[35,68,84,142,154],"by":[36,148],"first":[37],"generating":[38],"cues":[40],"localize":[42],"the":[43,49,67,79,102],"target":[44],"region":[45,111],"then":[47],"executing":[48],"edit.":[50],"Unlike":[51],"prior":[52],"text-only":[53],"CoT":[54,58],"or":[55],"tool-dependent":[56],"paradigms,":[59],"GVCoT":[60,180,192],"jointly":[61],"optimizes":[62],"tokens":[64],"generated":[65],"during":[66],"phases":[71],"in":[72,101,141],"an":[73],"end-to-end":[74],"manner.":[75],"This":[76],"way":[77],"fosters":[78],"emergence":[80],"of":[81,91,97,104,121],"innate":[82],"ability":[85,140],"enables":[87],"more":[88],"effective":[89],"utilization":[90],"visual-domain":[92],"cues.":[93],"The":[94],"main":[95],"challenge":[96],"training":[98,132],"GCVoT":[99],"lies":[100],"scarcity":[103],"large-scale":[105],"data":[107],"with":[108],"precise":[109,200],"edit":[110],"annotations;":[112],"end,":[115],"construct":[117],"GVCoT-Edit-Instruct,":[118],"dataset":[120],"1.8M":[122],"high-quality":[123],"samples":[124],"spanning":[125],"19":[126],"tasks.":[127],"We":[128,189],"adopt":[129],"progressive":[131],"strategy:":[133],"supervised":[134],"fine-tuning":[135],"build":[137],"foundational":[138],"localization":[139],"trace":[143],"before":[144],"final":[145],"editing,":[146],"followed":[147],"reinforcement":[149],"learning":[150],"further":[152],"improve":[153],"quality.":[157],"Finally,":[158],"introduce":[160],"SREdit-Bench,":[161],"new":[163],"benchmark":[164],"designed":[165],"comprehensively":[167],"stress-test":[168],"models":[169,184],"sophisticated":[171],"fine-grained":[174],"referring":[175],"expressions.":[176],"Experiments":[177],"demonstrate":[178],"consistently":[181],"outperforms":[182],"state-of-the-art":[183],"on":[185],"SREdit-Bench":[186],"ImgEdit.":[188],"hope":[190],"our":[191],"will":[193],"inspire":[194],"future":[195],"research":[196],"toward":[197],"interpretable":[198],"editing.":[202]},"counts_by_year":[],"updated_date":"2026-03-04T07:09:34.246503","created_date":"2026-03-04T00:00:00"}
