{"id":"https://openalex.org/W4312663267","doi":"https://doi.org/10.1109/icpr56361.2022.9956641","title":"Improving Weakly Supervised Scene Graph Parsing through Object Grounding","display_name":"Improving Weakly Supervised Scene Graph Parsing through Object Grounding","publication_year":2022,"publication_date":"2022-08-21","ids":{"openalex":"https://openalex.org/W4312663267","doi":"https://doi.org/10.1109/icpr56361.2022.9956641"},"language":"en","primary_location":{"id":"doi:10.1109/icpr56361.2022.9956641","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956641","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","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":"2022 26th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100643391","display_name":"Yizhou Zhang","orcid":"https://orcid.org/0000-0002-8206-4694"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yizhou Zhang","raw_affiliation_strings":["University of Southern California,Department of Computer Science,Los Angeles,California,90089"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Southern California,Department of Computer Science,Los Angeles,California,90089","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084866642","display_name":"Zhaoheng Zheng","orcid":null},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhaoheng Zheng","raw_affiliation_strings":["University of Southern California,Department of Computer Science,Los Angeles,California,90089"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Southern California,Department of Computer Science,Los Angeles,California,90089","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080385311","display_name":"Ram Nevatia","orcid":null},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ram Nevatia","raw_affiliation_strings":["University of Southern California,Department of Computer Science,Los Angeles,California,90089"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Southern California,Department of Computer Science,Los Angeles,California,90089","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100351175","display_name":"Yan Liu","orcid":"https://orcid.org/0000-0003-4242-4840"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yan Liu","raw_affiliation_strings":["University of Southern California,Department of Computer Science,Los Angeles,California,90089"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Southern California,Department of Computer Science,Los Angeles,California,90089","institution_ids":["https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I1174212"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16762115,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4058","last_page":"4064"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":1.0,"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":1.0,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","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"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.7882407903671265},{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.7047553658485413},{"id":"https://openalex.org/keywords/scene-graph","display_name":"Scene graph","score":0.6761723160743713},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6678448915481567},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6062735319137573},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45432770252227783},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.44479885697364807},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.4379054307937622},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3966698944568634},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2857663631439209}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7882407903671265},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.7047553658485413},{"id":"https://openalex.org/C179372163","wikidata":"https://www.wikidata.org/wiki/Q1406181","display_name":"Scene graph","level":3,"score":0.6761723160743713},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6678448915481567},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6062735319137573},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45432770252227783},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.44479885697364807},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.4379054307937622},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3966698944568634},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2857663631439209},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr56361.2022.9956641","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956641","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","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":"2022 26th International Conference on Pattern Recognition (ICPR)","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":49,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W2110119381","https://openalex.org/W2115672776","https://openalex.org/W2222512263","https://openalex.org/W2277195237","https://openalex.org/W2479423890","https://openalex.org/W2531897166","https://openalex.org/W2541678333","https://openalex.org/W2579549467","https://openalex.org/W2807021761","https://openalex.org/W2886970679","https://openalex.org/W2950096400","https://openalex.org/W2955988340","https://openalex.org/W2962785943","https://openalex.org/W2963101956","https://openalex.org/W2963184176","https://openalex.org/W2963536419","https://openalex.org/W2963762755","https://openalex.org/W2963938081","https://openalex.org/W2963980128","https://openalex.org/W2964015378","https://openalex.org/W2964727037","https://openalex.org/W2986803748","https://openalex.org/W2994860160","https://openalex.org/W2996731039","https://openalex.org/W3012644407","https://openalex.org/W3012663529","https://openalex.org/W3034538190","https://openalex.org/W3034910302","https://openalex.org/W3036686441","https://openalex.org/W3096209122","https://openalex.org/W3100848837","https://openalex.org/W3108230874","https://openalex.org/W3108864070","https://openalex.org/W3109988120","https://openalex.org/W3120329650","https://openalex.org/W4221149941","https://openalex.org/W4288083516","https://openalex.org/W4297783004","https://openalex.org/W6620707391","https://openalex.org/W6677467840","https://openalex.org/W6726873649","https://openalex.org/W6758719224","https://openalex.org/W6771848067","https://openalex.org/W6771924513","https://openalex.org/W6784986635","https://openalex.org/W6810026998","https://openalex.org/W6811047046"],"related_works":["https://openalex.org/W2068608913","https://openalex.org/W2050340680","https://openalex.org/W1700641177","https://openalex.org/W2953384362","https://openalex.org/W2754155766","https://openalex.org/W4287854977","https://openalex.org/W2963192850","https://openalex.org/W2769151336","https://openalex.org/W4392007279","https://openalex.org/W4387129494"],"abstract_inverted_index":{"Weakly":[0],"supervised":[1,55,74,86,156],"scene":[2,56,75,96,137,157,185],"graph":[3,14,48,57,76,97,124,138,158,186],"parsing,":[4],"which":[5],"learns":[6,89],"structured":[7],"image":[8],"representations":[9],"without":[10],"annotated":[11],"correspondences":[12,44],"between":[13,45],"nodes":[15,49,98],"and":[16,47,60,95,106,120,171],"visual":[17,81,93],"objects,":[18],"has":[19],"been":[20],"prevalent":[21],"in":[22],"recent":[23],"computer":[24],"vision":[25],"research.":[26],"Existing":[27],"methods":[28],"mainly":[29],"focus":[30],"on":[31,135,167,184],"designing":[32],"task-specific":[33],"loss":[34],"functions,":[35],"model":[36,128,147,180],"architectures,":[37],"or":[38],"optimization":[39],"algorithms.":[40],"We":[41],"argue":[42],"that":[43,71,178],"objects":[46,94],"are":[50,61],"crucial":[51],"for":[52],"the":[53,116,129,136,142,150,153,162],"weakly":[54,73,85,155],"parsing":[58,77,139,159],"task":[59,140,188],"worth":[62],"learning":[63,113],"explicitly.":[64],"Thus":[65],"we":[66,110],"propose":[67],"GroParser,":[68],"a":[69,90,122],"framework":[70],"improves":[72],"models":[78],"by":[79,99,145],"grounding":[80,87,143,187],"objects.":[82],"The":[83],"proposed":[84],"method":[88],"metric":[91],"among":[92],"incorporating":[100],"information":[101,119],"from":[102],"both":[103],"object":[104,117],"features":[105],"relational":[107,130],"features.":[108],"Specifically,":[109],"apply":[111],"multi-instance":[112],"to":[114,127],"learn":[115],"category":[118],"exploit":[121],"two-stream":[123],"neural":[125],"network":[126],"similarity":[131],"metric.":[132],"Extensive":[133],"experiments":[134,166],"verify":[141,177],"found":[144],"our":[146,179],"can":[148],"reinforce":[149],"performance":[151],"of":[152],"existing":[154,190],"methods,":[160],"including":[161],"current":[163],"state-of-the-art.":[164],"Further":[165],"Visual":[168,172],"Genome":[169],"(VG)":[170],"Relation":[173],"Detection":[174],"(VRD)":[175],"datasets":[176],"brings":[181],"an":[182],"improvement":[183],"over":[189],"approaches.":[191]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
