{"id":"https://openalex.org/W4312693141","doi":"https://doi.org/10.1109/tgrs.2022.3231351","title":"Class-Incremental Semantic Segmentation of Aerial Images via Pixel-Level Feature Generation and Task-Wise Distillation","display_name":"Class-Incremental Semantic Segmentation of Aerial Images via Pixel-Level Feature Generation and Task-Wise Distillation","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4312693141","doi":"https://doi.org/10.1109/tgrs.2022.3231351"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2022.3231351","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3231351","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-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/A5010380116","display_name":"Lianlei Shan","orcid":"https://orcid.org/0000-0002-4648-8246"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lianlei Shan","raw_affiliation_strings":["School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-4648-8246","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100721601","display_name":"Weiqiang Wang","orcid":"https://orcid.org/0000-0002-7185-2804"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiqiang Wang","raw_affiliation_strings":["School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-7185-2804","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109605874","display_name":"Ke Lv","orcid":"https://orcid.org/0000-0003-0176-3088"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ke Lv","raw_affiliation_strings":["School of Engineering Science, University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-0176-3088","affiliations":[{"raw_affiliation_string":"School of Engineering Science, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100372676","display_name":"Bin Luo","orcid":"https://orcid.org/0000-0001-5948-5055"},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Luo","raw_affiliation_strings":["MOE Key Laboratory of Signal Processing and Intelligent Computing, School of Computer Science and Technology, Anhui University, Hefei, China"],"raw_orcid":"https://orcid.org/0000-0001-5948-5055","affiliations":[{"raw_affiliation_string":"MOE Key Laboratory of Signal Processing and Intelligent Computing, School of Computer Science and Technology, Anhui University, Hefei, China","institution_ids":["https://openalex.org/I143868143"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.0676,"has_fulltext":false,"cited_by_count":31,"citation_normalized_percentile":{"value":0.94629969,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"60","issue":null,"first_page":"1","last_page":"17"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9998999834060669,"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"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9990000128746033,"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":0.9975000023841858,"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":0.8149135112762451},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7038335800170898},{"id":"https://openalex.org/keywords/forgetting","display_name":"Forgetting","score":0.7011388540267944},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.697289764881134},{"id":"https://openalex.org/keywords/softmax-function","display_name":"Softmax function","score":0.6110057830810547},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.568859338760376},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5194644331932068},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5140003561973572},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4951120913028717},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4902217984199524},{"id":"https://openalex.org/keywords/incremental-learning","display_name":"Incremental learning","score":0.4580707550048828},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4373979866504669},{"id":"https://openalex.org/keywords/memory-footprint","display_name":"Memory footprint","score":0.43106651306152344},{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.4302099943161011},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4204675853252411},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4018157124519348}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8149135112762451},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7038335800170898},{"id":"https://openalex.org/C7149132","wikidata":"https://www.wikidata.org/wiki/Q1377840","display_name":"Forgetting","level":2,"score":0.7011388540267944},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.697289764881134},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.6110057830810547},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.568859338760376},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5194644331932068},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5140003561973572},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4951120913028717},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4902217984199524},{"id":"https://openalex.org/C2780735816","wikidata":"https://www.wikidata.org/wiki/Q28324931","display_name":"Incremental learning","level":2,"score":0.4580707550048828},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4373979866504669},{"id":"https://openalex.org/C74912251","wikidata":"https://www.wikidata.org/wiki/Q6815727","display_name":"Memory footprint","level":2,"score":0.43106651306152344},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.4302099943161011},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4204675853252411},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4018157124519348},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2022.3231351","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3231351","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2231845458","display_name":null,"funder_award_id":"61860206004","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5516457395","display_name":null,"funder_award_id":"61976201","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":80,"referenced_works":["https://openalex.org/W1614298861","https://openalex.org/W1821462560","https://openalex.org/W2060277733","https://openalex.org/W2108598243","https://openalex.org/W2125389028","https://openalex.org/W2160684493","https://openalex.org/W2186094539","https://openalex.org/W2194775991","https://openalex.org/W2342880667","https://openalex.org/W2467604901","https://openalex.org/W2473930607","https://openalex.org/W2560023338","https://openalex.org/W2560647685","https://openalex.org/W2583761661","https://openalex.org/W2630837129","https://openalex.org/W2786446225","https://openalex.org/W2786498526","https://openalex.org/W2787091153","https://openalex.org/W2788388592","https://openalex.org/W2804199516","https://openalex.org/W2807445254","https://openalex.org/W2884282566","https://openalex.org/W2899867883","https://openalex.org/W2924476266","https://openalex.org/W2946824094","https://openalex.org/W2949543584","https://openalex.org/W2952716587","https://openalex.org/W2955058313","https://openalex.org/W2963073398","https://openalex.org/W2963201933","https://openalex.org/W2963283377","https://openalex.org/W2963960318","https://openalex.org/W2964067969","https://openalex.org/W2964189064","https://openalex.org/W2972313371","https://openalex.org/W2974317861","https://openalex.org/W2974777596","https://openalex.org/W2982410595","https://openalex.org/W3011986058","https://openalex.org/W3034435444","https://openalex.org/W3046253174","https://openalex.org/W3049293589","https://openalex.org/W3088431063","https://openalex.org/W3098511564","https://openalex.org/W3101570898","https://openalex.org/W3101808799","https://openalex.org/W3106029750","https://openalex.org/W3112561780","https://openalex.org/W3121842289","https://openalex.org/W3121873598","https://openalex.org/W3130523820","https://openalex.org/W3134946523","https://openalex.org/W3159637683","https://openalex.org/W3160470303","https://openalex.org/W3171427178","https://openalex.org/W3174396556","https://openalex.org/W3175270254","https://openalex.org/W3175350465","https://openalex.org/W3200754507","https://openalex.org/W3204291495","https://openalex.org/W3207978380","https://openalex.org/W3214620214","https://openalex.org/W4206158284","https://openalex.org/W4214924370","https://openalex.org/W4226013274","https://openalex.org/W4226462473","https://openalex.org/W4297665946","https://openalex.org/W6636510571","https://openalex.org/W6677258307","https://openalex.org/W6678815747","https://openalex.org/W6720208624","https://openalex.org/W6732467815","https://openalex.org/W6739696289","https://openalex.org/W6741217325","https://openalex.org/W6744056753","https://openalex.org/W6748453352","https://openalex.org/W6755312952","https://openalex.org/W6763251565","https://openalex.org/W6775845032","https://openalex.org/W6786443984"],"related_works":["https://openalex.org/W2946100827","https://openalex.org/W3186262193","https://openalex.org/W3192176272","https://openalex.org/W4297634446","https://openalex.org/W4381322349","https://openalex.org/W3085307110","https://openalex.org/W3157400488","https://openalex.org/W4382021137","https://openalex.org/W3108343974","https://openalex.org/W3034933965"],"abstract_inverted_index":{"Deep":[0],"neural":[1],"networks":[2,18],"achieve":[3,233],"significant":[4],"progress":[5],"in":[6,102,146],"semantic":[7],"segmentation":[8,36,104,127],"but":[9,116],"still":[10],"suffer":[11],"from":[12],"the":[13,45,56,63,82,87,94,99,111,126,130,147,154,163,207,211,218],"catastrophic":[14,48],"forgetting":[15],"problem,":[16],"i.e.,":[17,55],"will":[19],"forget":[20],"old":[21,40,83,88,160],"classes":[22,84,145,158,161],"as":[23],"they":[24],"learn":[25],"new":[26,157],"ones.":[27],"In":[28],"this":[29],"article,":[30],"we":[31,50],"propose":[32],"an":[33],"effective":[34,180],"class-incremental":[35,103],"framework":[37,220],"without":[38],"storing":[39],"data.":[41],"Specifically,":[42],"to":[43,74,85,97,117,159,232],"alleviate":[44],"issue":[46],"of":[47,79,81,105,156],"forgetting,":[49],"present":[51],"two":[52],"important":[53],"modules,":[54],"Pixel-level":[57],"Feature":[58],"Generation":[59],"(PFG)":[60],"module,":[61],"and":[62,108,141,181,191,210],"Task-wise":[64],"Knowledge":[65],"distillation":[66,169],"(TKD)":[67],"module.":[68],"The":[69,90,202],"PFG":[70,91,208],"module":[71,92,133,209,213],"is":[72,93,122,134,165,178],"designed":[73,136],"constantly":[75],"generate":[76,118],"any":[77,226],"number":[78],"features":[80],"keep":[86],"memory.":[89],"first":[95],"attempt":[96],"use":[98],"generative":[100],"method":[101,177,231],"aerial":[106],"images,":[107],"it":[109,142],"abandons":[110],"previous":[112],"image":[113],"generation":[114],"approach":[115],"pixel-level":[119],"features,":[120],"which":[121],"more":[123,170,183,192],"suitable":[124],"for":[125,137],"task.":[128],"Meanwhile,":[129],"proposed":[131,219],"TKD":[132,212],"specially":[135],"class":[138,228],"incremental":[139,229],"tasks,":[140],"only":[143],"compares":[144],"same":[148],"learning":[149,200,230],"step":[150],"(task),":[151],"thus":[152],"avoiding":[153],"squeezing":[155],"when":[162],"output":[164],"normalized":[166],"(softmax),":[167],"making":[168],"effective.":[171],"Sufficient":[172],"experiments":[173],"show":[174,205],"that":[175,206],"our":[176],"remarkably":[179],"achieves":[182],"than":[184,193],"4.5%":[185],"gains":[186],"compared":[187,195],"with":[188,196,225],"state-of-the-art":[189],"methods,":[190],"13%":[194],"baselines,":[197],"on":[198],"all":[199],"conditions.":[201],"ablation":[203],"studies":[204],"are":[214],"both":[215],"indispensables.":[216],"Besides,":[217],"can":[221],"be":[222],"well":[223],"combined":[224],"existing":[227],"better":[234],"performance.":[235]},"counts_by_year":[{"year":2025,"cited_by_count":16},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":7}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}