{"id":"https://openalex.org/W4226314502","doi":"https://doi.org/10.1109/icip46576.2022.9898031","title":"Robust and Accurate Object Detection Via Self-Knowledge Distillation","display_name":"Robust and Accurate Object Detection Via Self-Knowledge Distillation","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4226314502","doi":"https://doi.org/10.1109/icip46576.2022.9898031"},"language":"en","primary_location":{"id":"doi:10.1109/icip46576.2022.9898031","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9898031","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","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 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.17023/12gc-m851","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102954665","display_name":"Weipeng Xu","orcid":"https://orcid.org/0000-0001-9548-5108"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Weipeng Xu","raw_affiliation_strings":["Shanghai Jiao Tong University,China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085756411","display_name":"Pengzhi Chu","orcid":"https://orcid.org/0009-0001-7768-811X"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengzhi Chu","raw_affiliation_strings":["Shanghai Jiao Tong University,China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059333085","display_name":"Renhao Xie","orcid":null},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Renhao Xie","raw_affiliation_strings":["Shanghai Jiao Tong University,China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017190330","display_name":"Xiongziyan Xiao","orcid":null},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiongziyan Xiao","raw_affiliation_strings":["Shanghai Jiao Tong University,China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060964250","display_name":"Hongcheng Huang","orcid":"https://orcid.org/0000-0002-4249-8658"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongcheng Huang","raw_affiliation_strings":["Shanghai Jiao Tong University,China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5102954665"],"corresponding_institution_ids":["https://openalex.org/I183067930"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.02769397,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"91","last_page":"95"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9990000128746033,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9990000128746033,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9966999888420105,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.980400025844574,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7556490898132324},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.7130002975463867},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6597393751144409},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6160810589790344},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.614326000213623},{"id":"https://openalex.org/keywords/pascal","display_name":"Pascal (unit)","score":0.599374532699585},{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.5461380481719971},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.470040500164032},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46747681498527527},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.365173876285553}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7556490898132324},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7130002975463867},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6597393751144409},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6160810589790344},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.614326000213623},{"id":"https://openalex.org/C75608658","wikidata":"https://www.wikidata.org/wiki/Q44395","display_name":"Pascal (unit)","level":2,"score":0.599374532699585},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.5461380481719971},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.470040500164032},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46747681498527527},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.365173876285553},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","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},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icip46576.2022.9898031","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9898031","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","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 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},{"id":"doi:10.17023/12gc-m851","is_oa":true,"landing_page_url":"https://doi.org/10.17023/12gc-m851","pdf_url":null,"source":{"id":"https://openalex.org/S7407051697","display_name":"IEEE RESOURCE CENTERS","issn_l":null,"issn":[],"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":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.17023/12gc-m851","is_oa":true,"landing_page_url":"https://doi.org/10.17023/12gc-m851","pdf_url":null,"source":{"id":"https://openalex.org/S7407051697","display_name":"IEEE RESOURCE CENTERS","issn_l":null,"issn":[],"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":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.4300000071525574,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W2031489346","https://openalex.org/W2108598243","https://openalex.org/W2194775991","https://openalex.org/W2565639579","https://openalex.org/W2604505099","https://openalex.org/W2962766617","https://openalex.org/W2963150697","https://openalex.org/W2963351448","https://openalex.org/W2964309882","https://openalex.org/W2981495453","https://openalex.org/W3035743198","https://openalex.org/W3110416571","https://openalex.org/W3112828655","https://openalex.org/W3175958943","https://openalex.org/W3176187859","https://openalex.org/W3176459575","https://openalex.org/W3182316408","https://openalex.org/W4293846201","https://openalex.org/W6637162671","https://openalex.org/W6640425456","https://openalex.org/W6747819456","https://openalex.org/W6772461460","https://openalex.org/W6774469542","https://openalex.org/W6779586474","https://openalex.org/W6784426856"],"related_works":["https://openalex.org/W4001956","https://openalex.org/W221938","https://openalex.org/W9190101","https://openalex.org/W1679810","https://openalex.org/W6486743","https://openalex.org/W8544886","https://openalex.org/W10828093","https://openalex.org/W9547545","https://openalex.org/W1368183","https://openalex.org/W17565578"],"abstract_inverted_index":{"Object":[0],"detection":[1],"has":[2],"achieved":[3],"promising":[4],"performance":[5,66],"on":[6,140,173,183],"clean":[7,20,41,96,178,194],"datasets,":[8],"but":[9,34],"how":[10],"to":[11,31,43,90],"achieve":[12],"better":[13,65],"tradeoff":[14],"between":[15,75,95],"the":[16,28,37,73,115,141,146,154],"adversarial":[17,79,106,159,188,201],"robustness":[18,45,202],"and":[19,78,105,131,143,157],"precision":[21,42,179,195],"is":[22,27],"still":[23],"under-explored.":[24],"Adversarial":[25],"training":[26,80,156,160,189],"mainstream":[29],"method":[30],"improve":[32],"robustness,":[33],"most":[35],"of":[36],"works":[38],"will":[39,208],"sacrifice":[40],"gain":[44],"than":[46,67],"standard":[47,155],"training.":[48],"In":[49],"this":[50],"paper,":[51],"we":[52,113],"propose":[53],"Unified":[54],"Decoupled":[55],"Feature":[56],"Alignment":[57],"(UDFA),":[58],"a":[59,119,127,132],"novel":[60],"fine-tuning":[61],"paradigm":[62],"which":[63],"achieves":[64],"existing":[68],"methods,":[69,190],"by":[70,122,180,196,203],"fully":[71],"exploring":[72],"combination":[74],"self-knowledge":[76,92,116,134],"distillation":[77,93,117,135],"for":[81,162],"object":[82,163],"detection.":[83,164],"We":[84],"first":[85],"use":[86],"decoupled":[87],"fore/back-ground":[88],"features":[89],"construct":[91],"branch":[94,125,130],"feature":[97,107],"representation":[98,108],"from":[99,109,118],"pretrained":[100],"detector":[101],"(served":[102],"as":[103],"teacher)":[104],"student":[110],"detector.":[111],"Then":[112],"explore":[114],"new":[120,133],"angle":[121],"decoupling":[123],"original":[124],"into":[126],"self-supervised":[128],"learning":[129],"branch.":[136],"With":[137],"extensive":[138],"experiments":[139],"PASCAL-VOC":[142],"MS-COCO":[144],"benchmarks,":[145],"evaluation":[147],"results":[148],"show":[149],"that":[150],"UDFA":[151],"can":[152],"surpass":[153],"state-of-the-art":[158],"methods":[161],"For":[165],"example,":[166],"compared":[167,185],"with":[168,175,186],"teacher":[169],"detector,":[170],"our":[171,191],"approach":[172,192],"GFLV2":[174],"ResNet-50":[176],"improves":[177,193],"2.2":[181],"AP":[182],"PASCAL-VOC;":[184],"SOTA":[187],"1.6":[197],"AP,":[198],"while":[199],"improving":[200],"0.5":[204],"AP.":[205],"Our":[206],"code":[207],"be":[209],"available":[210],"at":[211],"https://github.com/grispeut/udfa.":[212]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2022-05-05T00:00:00"}
