{"id":"https://openalex.org/W4401417284","doi":"https://doi.org/10.1109/icra57147.2024.10610820","title":"Cycle-Correspondence Loss: Learning Dense View-Invariant Visual Features from Unlabeled and Unordered RGB Images","display_name":"Cycle-Correspondence Loss: Learning Dense View-Invariant Visual Features from Unlabeled and Unordered RGB Images","publication_year":2024,"publication_date":"2024-05-13","ids":{"openalex":"https://openalex.org/W4401417284","doi":"https://doi.org/10.1109/icra57147.2024.10610820"},"language":"en","primary_location":{"id":"doi:10.1109/icra57147.2024.10610820","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icra57147.2024.10610820","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Robotics and Automation (ICRA)","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/A5021651868","display_name":"David B. Adrian","orcid":"https://orcid.org/0000-0003-3964-6506"},"institutions":[{"id":"https://openalex.org/I889804353","display_name":"Robert Bosch (Germany)","ror":"https://ror.org/01fe0jt45","country_code":"DE","type":"company","lineage":["https://openalex.org/I889804353"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"David B. Adrian","raw_affiliation_strings":["Bosch Center for Artificial Intelligence,Renningen,Germany"],"affiliations":[{"raw_affiliation_string":"Bosch Center for Artificial Intelligence,Renningen,Germany","institution_ids":["https://openalex.org/I889804353"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014691067","display_name":"Andras Kupcsik","orcid":null},"institutions":[{"id":"https://openalex.org/I889804353","display_name":"Robert Bosch (Germany)","ror":"https://ror.org/01fe0jt45","country_code":"DE","type":"company","lineage":["https://openalex.org/I889804353"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Andras Gabor Kupcsik","raw_affiliation_strings":["Bosch Center for Artificial Intelligence,Renningen,Germany"],"affiliations":[{"raw_affiliation_string":"Bosch Center for Artificial Intelligence,Renningen,Germany","institution_ids":["https://openalex.org/I889804353"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058177091","display_name":"Markus Spies","orcid":null},"institutions":[{"id":"https://openalex.org/I889804353","display_name":"Robert Bosch (Germany)","ror":"https://ror.org/01fe0jt45","country_code":"DE","type":"company","lineage":["https://openalex.org/I889804353"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Markus Spies","raw_affiliation_strings":["Bosch Center for Artificial Intelligence,Renningen,Germany"],"affiliations":[{"raw_affiliation_string":"Bosch Center for Artificial Intelligence,Renningen,Germany","institution_ids":["https://openalex.org/I889804353"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052301598","display_name":"Heiko Neumann","orcid":"https://orcid.org/0000-0001-7687-5792"},"institutions":[{"id":"https://openalex.org/I196349391","display_name":"Universit\u00e4t Ulm","ror":"https://ror.org/032000t02","country_code":"DE","type":"education","lineage":["https://openalex.org/I196349391"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Heiko Neumann","raw_affiliation_strings":["Ulm University,Institute of Neural Information Processing,Ulm,Germany"],"affiliations":[{"raw_affiliation_string":"Ulm University,Institute of Neural Information Processing,Ulm,Germany","institution_ids":["https://openalex.org/I196349391"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5021651868"],"corresponding_institution_ids":["https://openalex.org/I889804353"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11953099,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"20","issue":null,"first_page":"11186","last_page":"11193"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9984999895095825,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9984999895095825,"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":0.9984999895095825,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9979000091552734,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.692767858505249},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.6522582769393921},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6368197202682495},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.5809274911880493},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5093086957931519},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3992602527141571},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.32727599143981934}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.692767858505249},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.6522582769393921},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6368197202682495},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.5809274911880493},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5093086957931519},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3992602527141571},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32727599143981934},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icra57147.2024.10610820","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icra57147.2024.10610820","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W2138621090","https://openalex.org/W2474531669","https://openalex.org/W2889977670","https://openalex.org/W2949678110","https://openalex.org/W2952069407","https://openalex.org/W2955368974","https://openalex.org/W2962697512","https://openalex.org/W2962793481","https://openalex.org/W2963419579","https://openalex.org/W2963823554","https://openalex.org/W2964700958","https://openalex.org/W2991276239","https://openalex.org/W3010688166","https://openalex.org/W3035235920","https://openalex.org/W3043075211","https://openalex.org/W3090004508","https://openalex.org/W3133699737","https://openalex.org/W3143092915","https://openalex.org/W3159481202","https://openalex.org/W3174459173","https://openalex.org/W4221155806","https://openalex.org/W4285102245","https://openalex.org/W4285102313","https://openalex.org/W4295312788","https://openalex.org/W4312779691","https://openalex.org/W4321319299","https://openalex.org/W4383108999","https://openalex.org/W4386821556","https://openalex.org/W6687483927","https://openalex.org/W6746200750","https://openalex.org/W6752823625","https://openalex.org/W6752936729","https://openalex.org/W6754539604","https://openalex.org/W6756257017","https://openalex.org/W6757817989","https://openalex.org/W6760546089","https://openalex.org/W6765456200","https://openalex.org/W6766978945","https://openalex.org/W6774314701","https://openalex.org/W6783068448","https://openalex.org/W6784173345","https://openalex.org/W6800436301","https://openalex.org/W6802465667","https://openalex.org/W6811470611","https://openalex.org/W6842914652","https://openalex.org/W6851800889"],"related_works":["https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2772917594","https://openalex.org/W2775347418","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"Robot":[0],"manipulation":[1,17],"relying":[2],"on":[3,84,123],"learned":[4,23],"object-centric":[5],"descriptors":[6,13],"became":[7],"popular":[8],"in":[9,41,114],"recent":[10],"years.":[11],"Visual":[12],"can":[14,21,29],"easily":[15],"describe":[16],"task":[18],"objectives,":[19],"they":[20,28],"be":[22],"efficiently":[24],"using":[25],"self-supervision,":[26],"and":[27,32,54,82,137],"encode":[30],"actuated":[31],"even":[33],"non-rigid":[34],"objects.":[35],"However,":[36],"learning":[37],"robust,":[38],"view-invariant":[39,66],"keypoints":[40],"a":[42,46,77,103,106,153],"self-supervised":[43,134],"approach":[44,50,138],"requires":[45],"meticulous":[47],"data":[48,79],"collection":[49,80],"involving":[51],"precise":[52],"calibration":[53],"expert":[55],"supervision.":[56],"In":[57],"this":[58],"paper":[59],"we":[60,131],"introduce":[61],"Cycle-Correspondence":[62],"Loss":[63],"(CCL)":[64],"for":[65,152],"dense":[67],"descriptor":[68],"learning,":[69],"which":[70],"adopts":[71],"the":[72,111,115,124],"concept":[73],"of":[74,140],"cycle-consistency,":[75],"enabling":[76],"simple":[78],"pipeline":[81],"training":[83],"unpaired":[85],"RGB":[86],"camera":[87],"views.":[88],"The":[89],"key":[90],"idea":[91],"is":[92],"to":[93,101,109,146],"autonomously":[94],"detect":[95],"valid":[96],"pixel":[97,113],"correspondences":[98],"by":[99],"attempting":[100],"use":[102],"prediction":[104],"over":[105],"new":[107],"image":[108],"predict":[110],"original":[112,116],"image,":[117],"while":[118],"scaling":[119],"error":[120],"terms":[121],"based":[122],"estimated":[125],"confidence.":[126],"Our":[127],"evaluation":[128],"shows":[129],"that":[130],"outperform":[132],"other":[133],"RGB-only":[135],"methods,":[136,142],"performance":[139],"supervised":[141],"both":[143],"with":[144],"respect":[145],"keypoint":[147],"tracking":[148],"as":[149,151],"well":[150],"robot":[154],"grasping":[155],"downstream":[156],"task.":[157]},"counts_by_year":[],"updated_date":"2025-12-21T01:58:51.020947","created_date":"2025-10-10T00:00:00"}
