{"id":"https://openalex.org/W3171112199","doi":"https://doi.org/10.1109/iccv48922.2021.00976","title":"Deep Matching Prior: Test-Time Optimization for Dense Correspondence","display_name":"Deep Matching Prior: Test-Time Optimization for Dense Correspondence","publication_year":2021,"publication_date":"2021-10-01","ids":{"openalex":"https://openalex.org/W3171112199","doi":"https://doi.org/10.1109/iccv48922.2021.00976","mag":"3171112199"},"language":"en","primary_location":{"id":"doi:10.1109/iccv48922.2021.00976","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv48922.2021.00976","pdf_url":null,"source":{"id":"https://openalex.org/S4363607764","display_name":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5061636336","display_name":"Sunghwan Hong","orcid":"https://orcid.org/0000-0003-0685-3779"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Sunghwan Hong","raw_affiliation_strings":["Korea University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Korea University","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085363061","display_name":"Seungryong Kim","orcid":"https://orcid.org/0000-0003-2927-6273"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Seungryong Kim","raw_affiliation_strings":["Korea University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Korea University","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5061636336"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7221,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.81318052,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"9887","last_page":"9897"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9998000264167786,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9998000264167786,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9997000098228455,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9994999766349792,"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/computer-science","display_name":"Computer science","score":0.8190410137176514},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.8011500239372253},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7171264290809631},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6023455858230591},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5980772376060486},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5956356525421143},{"id":"https://openalex.org/keywords/test-data","display_name":"Test data","score":0.49188852310180664},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.490771621465683},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4869500398635864},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.41650527715682983},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41197025775909424},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.1556244194507599},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09180474281311035}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8190410137176514},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.8011500239372253},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7171264290809631},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6023455858230591},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5980772376060486},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5956356525421143},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.49188852310180664},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.490771621465683},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4869500398635864},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.41650527715682983},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41197025775909424},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.1556244194507599},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09180474281311035},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccv48922.2021.00976","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv48922.2021.00976","pdf_url":null,"source":{"id":"https://openalex.org/S4363607764","display_name":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/1","score":0.5299999713897705,"display_name":"No poverty"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":118,"referenced_works":["https://openalex.org/W54257720","https://openalex.org/W1491719799","https://openalex.org/W1522301498","https://openalex.org/W1686810756","https://openalex.org/W1964057156","https://openalex.org/W1989191365","https://openalex.org/W2085261163","https://openalex.org/W2090518410","https://openalex.org/W2108598243","https://openalex.org/W2124861766","https://openalex.org/W2124964692","https://openalex.org/W2138621090","https://openalex.org/W2151103935","https://openalex.org/W2194775991","https://openalex.org/W2320444803","https://openalex.org/W2439114332","https://openalex.org/W2464606141","https://openalex.org/W2558625610","https://openalex.org/W2560474170","https://openalex.org/W2566812041","https://openalex.org/W2593948489","https://openalex.org/W2604231069","https://openalex.org/W2604233003","https://openalex.org/W2611605760","https://openalex.org/W2741885505","https://openalex.org/W2752461516","https://openalex.org/W2785523195","https://openalex.org/W2842511635","https://openalex.org/W2886782161","https://openalex.org/W2891202958","https://openalex.org/W2897093986","https://openalex.org/W2899771611","https://openalex.org/W2950974964","https://openalex.org/W2962770929","https://openalex.org/W2963020784","https://openalex.org/W2963325280","https://openalex.org/W2963360726","https://openalex.org/W2963619659","https://openalex.org/W2963704386","https://openalex.org/W2963748588","https://openalex.org/W2963760790","https://openalex.org/W2963782415","https://openalex.org/W2963827464","https://openalex.org/W2963995996","https://openalex.org/W2964013315","https://openalex.org/W2964073646","https://openalex.org/W2964141676","https://openalex.org/W2964188292","https://openalex.org/W2968917279","https://openalex.org/W2971252756","https://openalex.org/W2982121679","https://openalex.org/W2982420602","https://openalex.org/W2985068832","https://openalex.org/W2990910330","https://openalex.org/W2994689640","https://openalex.org/W2995801453","https://openalex.org/W3005680577","https://openalex.org/W3009561768","https://openalex.org/W3011520397","https://openalex.org/W3013170474","https://openalex.org/W3034915791","https://openalex.org/W3035060554","https://openalex.org/W3035242260","https://openalex.org/W3035472788","https://openalex.org/W3035477606","https://openalex.org/W3035524453","https://openalex.org/W3035570181","https://openalex.org/W3035574324","https://openalex.org/W3035578028","https://openalex.org/W3036255508","https://openalex.org/W3037618862","https://openalex.org/W3043753768","https://openalex.org/W3092135995","https://openalex.org/W3094761124","https://openalex.org/W3103187163","https://openalex.org/W3104662889","https://openalex.org/W3106528393","https://openalex.org/W3106539090","https://openalex.org/W3106982106","https://openalex.org/W3107096356","https://openalex.org/W3109908659","https://openalex.org/W3117585461","https://openalex.org/W3168822201","https://openalex.org/W4205425515","https://openalex.org/W4287600707","https://openalex.org/W4287666213","https://openalex.org/W4297808394","https://openalex.org/W4301454730","https://openalex.org/W4387928059","https://openalex.org/W6602211262","https://openalex.org/W6629564929","https://openalex.org/W6631190155","https://openalex.org/W6637373629","https://openalex.org/W6678856934","https://openalex.org/W6700340091","https://openalex.org/W6718003766","https://openalex.org/W6743895910","https://openalex.org/W6746466744","https://openalex.org/W6747620207","https://openalex.org/W6753633550","https://openalex.org/W6754783729","https://openalex.org/W6754984195","https://openalex.org/W6764386301","https://openalex.org/W6767164110","https://openalex.org/W6768009244","https://openalex.org/W6771820816","https://openalex.org/W6774314701","https://openalex.org/W6774670964","https://openalex.org/W6775583579","https://openalex.org/W6776990516","https://openalex.org/W6779326418","https://openalex.org/W6779835006","https://openalex.org/W6780180733","https://openalex.org/W6780757586","https://openalex.org/W6781536066","https://openalex.org/W6783308292","https://openalex.org/W6786093290","https://openalex.org/W6786187962"],"related_works":["https://openalex.org/W2560215812","https://openalex.org/W2949601986","https://openalex.org/W2788972299","https://openalex.org/W1630076647","https://openalex.org/W4375867731","https://openalex.org/W2371138613","https://openalex.org/W2521347458","https://openalex.org/W2080152487","https://openalex.org/W2498789492","https://openalex.org/W2048963458"],"abstract_inverted_index":{"Conventional":[0],"techniques":[1],"to":[2,22,34,97,144],"establish":[3],"dense":[4,131],"correspondences":[5],"across":[6],"visually":[7],"or":[8,161],"semantically":[9],"similar":[10],"images":[11,84],"focused":[12],"on":[13,44,119,169,197],"designing":[14],"a":[15,36,41,81,135,140,146],"task-specific":[16],"matching":[17,38,117,137,174],"prior,":[18,89],"which":[19],"is":[20,159],"difficult":[21],"model":[23,42,70],"in":[24],"general.":[25],"To":[26],"overcome":[27],"this,":[28],"recent":[29],"learning-based":[30,167],"methods":[31,168],"have":[32],"attempted":[33],"learn":[35],"good":[37],"prior":[39,108],"within":[40],"itself":[43],"large":[45,183],"training":[46,58,184],"data.":[47],"The":[48],"performance":[49,93,196],"improvement":[50],"was":[51],"apparent,":[52],"but":[53],"the":[54,68,78,115,165,190],"need":[55],"for":[56,77,126,130,172],"sufficient":[57],"data":[59,185],"and":[60,94,139,175],"intensive":[61,187],"learning":[62],"hinders":[63],"their":[64,87],"applicability.":[65],"Moreover,":[66],"using":[67],"fixed":[69],"at":[71],"test":[72],"time":[73],"does":[74],"not":[75],"account":[76],"fact":[79],"that":[80,104,151],"pair":[82,122],"of":[83,123],"may":[85],"require":[86],"own":[88],"thus":[90],"providing":[91],"limited":[92],"poor":[95],"generalization":[96],"unseen":[98],"images.In":[99],"this":[100],"paper,":[101],"we":[102,133],"show":[103],"an":[105,120],"image":[106],"pair-specific":[107],"can":[109],"be":[110],"captured":[111],"by":[112],"solely":[113],"optimizing":[114],"untrained":[116],"networks":[118,191],"input":[121],"images.":[124],"Tailored":[125],"such":[127],"test-time":[128],"optimization":[129],"correspondence,":[132],"present":[134],"residual":[136],"network":[138],"confidence-aware":[141],"contrastive":[142],"loss":[143],"guarantee":[145],"meaningful":[147],"convergence.":[148],"Experiments":[149],"demonstrate":[150],"our":[152],"framework,":[153],"dubbed":[154],"Deep":[155],"Matching":[156],"Prior":[157],"(DMP),":[158],"competitive,":[160],"even":[162,178],"outperforms,":[163],"against":[164],"latest":[166],"several":[170],"benchmarks":[171],"geometric":[173],"semantic":[176],"matching,":[177],"though":[179],"it":[180],"requires":[181],"neither":[182],"nor":[186],"learning.":[188],"With":[189],"pre-trained,":[192],"DMP":[193],"attains":[194],"state-of-the-art":[195],"all":[198],"benchmarks.":[199]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":4}],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2025-10-10T00:00:00"}
