{"id":"https://openalex.org/W2988102470","doi":"https://doi.org/10.1109/igarss.2019.8898722","title":"A simple rotational equivariance loss for generic convolutional segmentation networks: preliminary results","display_name":"A simple rotational equivariance loss for generic convolutional segmentation networks: preliminary results","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2988102470","doi":"https://doi.org/10.1109/igarss.2019.8898722","mag":"2988102470"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2019.8898722","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8898722","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","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/A5113587600","display_name":"Kangcheng Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Kangcheng Lin","raw_affiliation_strings":["Department of Statistical Science, Duke University, Durham, NC"],"affiliations":[{"raw_affiliation_string":"Department of Statistical Science, Duke University, Durham, NC","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084623752","display_name":"Bohao Huang","orcid":"https://orcid.org/0000-0002-6658-4366"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bohao Huang","raw_affiliation_strings":["Department of Electrical & Computer Engineering, Duke University, Durham, NC"],"affiliations":[{"raw_affiliation_string":"Department of Electrical & Computer Engineering, Duke University, Durham, NC","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004578492","display_name":"Leslie M. Collins","orcid":"https://orcid.org/0000-0002-9043-6531"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Leslie M. Collins","raw_affiliation_strings":["Department of Electrical & Computer Engineering, Duke University, Durham, NC"],"affiliations":[{"raw_affiliation_string":"Department of Electrical & Computer Engineering, Duke University, Durham, NC","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054438743","display_name":"Kyle Bradbury","orcid":"https://orcid.org/0000-0001-9847-0243"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kyle Bradbury","raw_affiliation_strings":["Department of Electrical & Computer Engineering, Duke University, Durham, NC"],"affiliations":[{"raw_affiliation_string":"Department of Electrical & Computer Engineering, Duke University, Durham, NC","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007239332","display_name":"Jordan M. Malof","orcid":"https://orcid.org/0000-0002-7851-4920"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jordan M. Malof","raw_affiliation_strings":["Department of Electrical & Computer Engineering, Duke University, Durham, NC"],"affiliations":[{"raw_affiliation_string":"Department of Electrical & Computer Engineering, Duke University, Durham, NC","institution_ids":["https://openalex.org/I170897317"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5113587600"],"corresponding_institution_ids":["https://openalex.org/I170897317"],"apc_list":null,"apc_paid":null,"fwci":0.4049,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.66197588,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9994000196456909,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9994000196456909,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9980999827384949,"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":0.7564378976821899},{"id":"https://openalex.org/keywords/equivariant-map","display_name":"Equivariant map","score":0.753901481628418},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6784911155700684},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6675488352775574},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.6324648261070251},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5591384172439575},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.553797721862793},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.5136846899986267},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.484894335269928},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4528459310531616},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3793269693851471},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3519161641597748},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16305840015411377},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07028108835220337},{"id":"https://openalex.org/keywords/pure-mathematics","display_name":"Pure mathematics","score":0.06437745690345764}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7564378976821899},{"id":"https://openalex.org/C171036898","wikidata":"https://www.wikidata.org/wiki/Q256355","display_name":"Equivariant map","level":2,"score":0.753901481628418},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6784911155700684},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6675488352775574},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.6324648261070251},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5591384172439575},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.553797721862793},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.5136846899986267},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.484894335269928},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4528459310531616},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3793269693851471},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3519161641597748},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16305840015411377},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07028108835220337},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.06437745690345764},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2019.8898722","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8898722","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7200000286102295,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2512351403","https://openalex.org/W2576915720","https://openalex.org/W2683784395","https://openalex.org/W2793461576","https://openalex.org/W2804199516","https://openalex.org/W2804962028","https://openalex.org/W6739684510"],"related_works":["https://openalex.org/W1992306031","https://openalex.org/W2517104666","https://openalex.org/W3034655717","https://openalex.org/W2005437358","https://openalex.org/W1669643531","https://openalex.org/W2337415362","https://openalex.org/W4299569200","https://openalex.org/W2008656436","https://openalex.org/W2546645752","https://openalex.org/W2134924024"],"abstract_inverted_index":{"Segmentation":[0],"convolutional":[1],"neural":[2],"networks":[3],"(SCNNs)":[4],"are":[5,89,97],"now":[6],"popular":[7,147],"for":[8],"the":[9,45,60,112,119,134,137,146,156,164],"semantic":[10],"segmentation":[11],"(i.e.,":[12],"dense":[13],"pixel-wise":[14],"labeling)":[15],"of":[16,30,150,155],"remote":[17,35],"sensing":[18,36],"imagery,":[19],"such":[20],"as":[21],"color":[22],"or":[23],"hyperspectral":[24],"satellite":[25],"imagery.":[26],"One":[27],"desirable":[28],"property":[29,42],"SCNNs":[31,79],"when":[32],"applied":[33],"to":[34,49,75,111,121,129,145],"problems":[37],"is":[38,63,126],"rotational":[39,77,153],"equivariance.":[40],"This":[41],"implies":[43],"that":[44,71,88,96,117,163],"class":[46],"label":[47],"assigned":[48],"a":[50,108],"particular":[51],"pixel":[52],"(building,":[53],"road,":[54],"etc.)":[55],"does":[56],"not":[57],"change":[58],"if":[59],"input":[61,157],"imagery":[62,158],"rotated":[64],"by":[65],"an":[66],"arbitrary":[67],"angle.":[68],"We":[69,106,132,161],"argue":[70],"recently":[72],"proposed":[73,165],"methods":[74,87,95],"make":[76],"equivariant":[78],"fall":[80],"into":[81],"two":[82],"broad":[83],"categories:":[84],"easily":[85,127],"employed":[86],"somewhat":[90],"ineffective,":[91],"and":[92,99,125,142,171],"highly":[93],"effective":[94],"complicated":[98],"potentially":[100],"incompatible":[101],"with":[102],"state-of-the-art":[103],"SCNN":[104,114,120],"techniques.":[105],"propose":[107],"simple":[109,148],"addition":[110],"standard":[113],"loss":[115],"function":[116],"encourages":[118],"be":[122],"rotationally":[123],"equivariant,":[124],"added":[128],"modern":[130],"SCNNs.":[131],"test":[133],"method":[135],"on":[136,176],"Inria":[138],"building":[139],"labeling":[140],"dataset":[141],"compare":[143],"it":[144],"approach":[149,166],"adding":[151],"random":[152],"augmentations":[154],"during":[159],"training.":[160],"show":[162],"(i)":[167],"achieves":[168],"improved":[169],"equivariance":[170],"(ii)":[172],"yields":[173],"performance":[174],"improvements":[175],"average.":[177]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}