{"id":"https://openalex.org/W3207394454","doi":"https://doi.org/10.1145/3474085.3481030","title":"Annotation-Efficient Semantic Segmentation with Shape Prior Knowledge","display_name":"Annotation-Efficient Semantic Segmentation with Shape Prior Knowledge","publication_year":2021,"publication_date":"2021-10-17","ids":{"openalex":"https://openalex.org/W3207394454","doi":"https://doi.org/10.1145/3474085.3481030","mag":"3207394454"},"language":"en","primary_location":{"id":"doi:10.1145/3474085.3481030","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3474085.3481030","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM International Conference on Multimedia","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/A5032017736","display_name":"Yuhang Lu","orcid":"https://orcid.org/0000-0002-1072-3443"},"institutions":[{"id":"https://openalex.org/I155781252","display_name":"University of South Carolina","ror":"https://ror.org/02b6qw903","country_code":"US","type":"education","lineage":["https://openalex.org/I155781252"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yuhang Lu","raw_affiliation_strings":["University of South Carolina, Columbia, SC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of South Carolina, Columbia, SC, USA","institution_ids":["https://openalex.org/I155781252"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5032017736"],"corresponding_institution_ids":["https://openalex.org/I155781252"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.2182719,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2955","last_page":"2959"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14510","display_name":"Medical Imaging and Analysis","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T14510","display_name":"Medical Imaging and Analysis","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9954000115394592,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.994700014591217,"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/segmentation","display_name":"Segmentation","score":0.8619749546051025},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7870871424674988},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.7146457433700562},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7014992237091064},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5663108825683594},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.536300778388977},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5265037417411804},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5166090130805969},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.512421190738678},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.47848981618881226},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.46627169847488403},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43911612033843994},{"id":"https://openalex.org/keywords/active-shape-model","display_name":"Active shape model","score":0.4301643967628479},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4209873080253601},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11358457803726196},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.07268798351287842}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.8619749546051025},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7870871424674988},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.7146457433700562},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7014992237091064},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5663108825683594},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.536300778388977},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5265037417411804},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5166090130805969},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.512421190738678},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.47848981618881226},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.46627169847488403},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43911612033843994},{"id":"https://openalex.org/C129641003","wikidata":"https://www.wikidata.org/wiki/Q267189","display_name":"Active shape model","level":3,"score":0.4301643967628479},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4209873080253601},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11358457803726196},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.07268798351287842},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3474085.3481030","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3474085.3481030","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2082765213","https://openalex.org/W2104095591","https://openalex.org/W2116040950","https://openalex.org/W2121947440","https://openalex.org/W2124351162","https://openalex.org/W2128409098","https://openalex.org/W2165734775","https://openalex.org/W2186222003","https://openalex.org/W2325368899","https://openalex.org/W2331128040","https://openalex.org/W2585890928","https://openalex.org/W2767798777","https://openalex.org/W2916670550","https://openalex.org/W2918771273","https://openalex.org/W2963730812","https://openalex.org/W2964216471","https://openalex.org/W2971013993","https://openalex.org/W3014641072","https://openalex.org/W3014795415","https://openalex.org/W3100175091","https://openalex.org/W3112707144","https://openalex.org/W3211330693","https://openalex.org/W4234552385","https://openalex.org/W4248635988"],"related_works":["https://openalex.org/W2361861616","https://openalex.org/W2263699433","https://openalex.org/W2580650124","https://openalex.org/W2377979023","https://openalex.org/W4386190339","https://openalex.org/W2968424575","https://openalex.org/W2218034408","https://openalex.org/W1990603712","https://openalex.org/W2250899144","https://openalex.org/W3016245616"],"abstract_inverted_index":{"Deep":[0],"learning":[1],"methods":[2,135,146],"have":[3],"achieved":[4],"great":[5],"success":[6],"on":[7,18,50,151],"semantic":[8],"segmentation":[9,44,89,110,128,145],"in":[10,29],"recent":[11],"years.":[12],"But":[13],"the":[14,36,40,52,59,75,95,117,152],"training":[15,46],"typically":[16],"relies":[17],"large-scale":[19],"fully-annotated":[20],"ground":[21],"truth":[22],"masks,":[23],"which":[24],"are":[25],"difficult":[26],"to":[27,82,104,115,120,123],"obtain":[28],"practice.":[30],"In":[31],"this":[32],"research,":[33],"we":[34,64,72,113],"study":[35,65],"problem":[37],"of":[38,43,56,61,68,79,98],"reducing":[39],"annotation":[41,149],"cost":[42],"network":[45],"with":[47,147],"a":[48,84,106,125],"focus":[49],"exploring":[51],"shape":[53,69,77,96,118],"prior":[54,78,97],"knowledge":[55],"objects.":[57],"Under":[58],"context":[60],"three":[62,66],"applications,":[63],"types":[67],"priors.":[70],"Specifically,":[71],"first":[73],"exploit":[74],"implicit":[76],"curve":[80,87],"structures":[81,100,109],"propose":[83,105,124],"weakly":[85],"supervised":[86,144],"structure":[88],"method,":[90],"and":[91],"then":[92],"explicitly":[93],"formulate":[94],"anatomical":[99,108],"as":[101],"loss":[102],"functions":[103],"one-shot":[107],"network.":[111],"Last,":[112],"try":[114],"generalize":[116],"constraint":[119],"arbitrary":[121],"objects":[122],"class-agnostic":[126],"few-shot":[127],"framework.":[129],"Experiment":[130],"results":[131],"show":[132],"that":[133],"our":[134],"could":[136],"achieve":[137],"comparable":[138],"or":[139],"better":[140],"performance":[141],"than":[142],"fully":[143],"less":[148],"costs":[150],"studied":[153],"applications.":[154]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}