{"id":"https://openalex.org/W3008809756","doi":"https://doi.org/10.1109/tip.2020.2973812","title":"The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification","display_name":"The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3008809756","doi":"https://doi.org/10.1109/tip.2020.2973812","mag":"3008809756","pmid":"https://pubmed.ncbi.nlm.nih.gov/32092002"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2020.2973812","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2020.2973812","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"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 Image Processing","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2002.04264","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5007417490","display_name":"Dongliang Chang","orcid":"https://orcid.org/0000-0002-4081-3001"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongliang Chang","raw_affiliation_strings":["Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-4081-3001","affiliations":[{"raw_affiliation_string":"Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027827007","display_name":"Yifeng Ding","orcid":"https://orcid.org/0000-0003-0324-4484"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yifeng Ding","raw_affiliation_strings":["Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-0324-4484","affiliations":[{"raw_affiliation_string":"Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048826896","display_name":"Jiyang Xie","orcid":"https://orcid.org/0000-0003-3659-9476"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiyang Xie","raw_affiliation_strings":["Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-3659-9476","affiliations":[{"raw_affiliation_string":"Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003628834","display_name":"Ayan Kumar Bhunia","orcid":"https://orcid.org/0000-0001-7928-7646"},"institutions":[{"id":"https://openalex.org/I28290843","display_name":"University of Surrey","ror":"https://ror.org/00ks66431","country_code":"GB","type":"education","lineage":["https://openalex.org/I28290843"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Ayan Kumar Bhunia","raw_affiliation_strings":["Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, U.K"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, U.K","institution_ids":["https://openalex.org/I28290843"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100758186","display_name":"Xiaoxu Li","orcid":"https://orcid.org/0000-0001-8833-9401"},"institutions":[{"id":"https://openalex.org/I22716506","display_name":"Lanzhou University of Technology","ror":"https://ror.org/03panb555","country_code":"CN","type":"education","lineage":["https://openalex.org/I22716506"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoxu Li","raw_affiliation_strings":["School of Computer and Communication, Lanzhou University of Technology, Lanzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-8833-9401","affiliations":[{"raw_affiliation_string":"School of Computer and Communication, Lanzhou University of Technology, Lanzhou, China","institution_ids":["https://openalex.org/I22716506"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039812471","display_name":"Zhanyu Ma","orcid":"https://orcid.org/0000-0003-2950-2488"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhanyu Ma","raw_affiliation_strings":["Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-2950-2488","affiliations":[{"raw_affiliation_string":"Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077902436","display_name":"Ming Wu","orcid":"https://orcid.org/0000-0001-8390-5398"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ming Wu","raw_affiliation_strings":["Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-8390-5398","affiliations":[{"raw_affiliation_string":"Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100445470","display_name":"Jun Guo","orcid":"https://orcid.org/0000-0001-9045-1339"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Guo","raw_affiliation_strings":["Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-9045-1339","affiliations":[{"raw_affiliation_string":"Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046046128","display_name":"Yi-Zhe Song","orcid":"https://orcid.org/0000-0001-5908-3275"},"institutions":[{"id":"https://openalex.org/I28290843","display_name":"University of Surrey","ror":"https://ror.org/00ks66431","country_code":"GB","type":"education","lineage":["https://openalex.org/I28290843"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Yi-Zhe Song","raw_affiliation_strings":["Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, U.K"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, U.K","institution_ids":["https://openalex.org/I28290843"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":25.1083,"has_fulltext":false,"cited_by_count":405,"citation_normalized_percentile":{"value":0.99735517,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"29","issue":null,"first_page":"4683","last_page":"4695"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9997000098228455,"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":0.9997000098228455,"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/discriminative-model","display_name":"Discriminative model","score":0.890092134475708},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6725335717201233},{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.6314340829849243},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6298484206199646},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6065621972084045},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.605096697807312},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.5977743864059448},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.5827674865722656},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.5223901867866516},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.49398666620254517},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.4737173914909363},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.29121720790863037},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.28402531147003174}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.890092134475708},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6725335717201233},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.6314340829849243},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6298484206199646},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6065621972084045},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.605096697807312},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.5977743864059448},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5827674865722656},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.5223901867866516},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.49398666620254517},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.4737173914909363},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.29121720790863037},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28402531147003174},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/tip.2020.2973812","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2020.2973812","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"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 Image Processing","raw_type":"journal-article"},{"id":"pmid:32092002","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/32092002","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","raw_type":null},{"id":"pmh:oai:arXiv.org:2002.04264","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2002.04264","pdf_url":"https://arxiv.org/pdf/2002.04264","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:ir.lzu.edu.cn/:262010/441538","is_oa":false,"landing_page_url":"http://ir.lzu.edu.cn/handle/262010/441538","pdf_url":null,"source":{"id":"https://openalex.org/S4406923049","display_name":"Lanzhou University Institutional Repository","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":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"\u671f\u520a\u8bba\u6587"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2002.04264","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2002.04264","pdf_url":"https://arxiv.org/pdf/2002.04264","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7099999785423279}],"awards":[{"id":"https://openalex.org/G1365127717","display_name":null,"funder_award_id":"L172030","funder_id":"https://openalex.org/F4320322919","funder_display_name":"Natural Science Foundation of Beijing Municipality"},{"id":"https://openalex.org/G2357889549","display_name":null,"funder_award_id":"61922015","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2391406392","display_name":null,"funder_award_id":"61563030","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3848188706","display_name":null,"funder_award_id":"U19B2036","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3900530946","display_name":null,"funder_award_id":"61773071","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6064151300","display_name":null,"funder_award_id":"2018ZX03001031","funder_id":"https://openalex.org/F4320320719","funder_display_name":"Department of Science and Technology, Ministry of Science and Technology, India"},{"id":"https://openalex.org/G7384186329","display_name":null,"funder_award_id":"61906080","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8314221504","display_name":null,"funder_award_id":"CSC 201906470049","funder_id":"https://openalex.org/F4320322725","funder_display_name":"China Scholarship Council"}],"funders":[{"id":"https://openalex.org/F4320320719","display_name":"Department of Science and Technology, Ministry of Science and Technology, India","ror":"https://ror.org/0101xrq71"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"},{"id":"https://openalex.org/F4320322919","display_name":"Natural Science Foundation of Beijing Municipality","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":64,"referenced_works":["https://openalex.org/W56385144","https://openalex.org/W1616462885","https://openalex.org/W1686810756","https://openalex.org/W1797268635","https://openalex.org/W1799366690","https://openalex.org/W1846799578","https://openalex.org/W1980526845","https://openalex.org/W2024368999","https://openalex.org/W2062118960","https://openalex.org/W2104657103","https://openalex.org/W2110015572","https://openalex.org/W2118696714","https://openalex.org/W2135706578","https://openalex.org/W2138011018","https://openalex.org/W2194775991","https://openalex.org/W2207849498","https://openalex.org/W2479109623","https://openalex.org/W2520774990","https://openalex.org/W2530289846","https://openalex.org/W2533598788","https://openalex.org/W2550553598","https://openalex.org/W2591924527","https://openalex.org/W2604397568","https://openalex.org/W2737725206","https://openalex.org/W2740620254","https://openalex.org/W2750672897","https://openalex.org/W2752782242","https://openalex.org/W2765268259","https://openalex.org/W2767623212","https://openalex.org/W2773003563","https://openalex.org/W2780838211","https://openalex.org/W2781110478","https://openalex.org/W2798365843","https://openalex.org/W2810809861","https://openalex.org/W2883888092","https://openalex.org/W2884561390","https://openalex.org/W2891951760","https://openalex.org/W2917452308","https://openalex.org/W2922521335","https://openalex.org/W2923501483","https://openalex.org/W2947109354","https://openalex.org/W2962858109","https://openalex.org/W2962898354","https://openalex.org/W2963166243","https://openalex.org/W2963351448","https://openalex.org/W2963393555","https://openalex.org/W2963407932","https://openalex.org/W2963420686","https://openalex.org/W2963466847","https://openalex.org/W2969362383","https://openalex.org/W2969597887","https://openalex.org/W2982863468","https://openalex.org/W2997736261","https://openalex.org/W3020197498","https://openalex.org/W4309845474","https://openalex.org/W6636475194","https://openalex.org/W6637373629","https://openalex.org/W6638319203","https://openalex.org/W6638444622","https://openalex.org/W6638677478","https://openalex.org/W6666023844","https://openalex.org/W6696761078","https://openalex.org/W6726946684","https://openalex.org/W6743186171"],"related_works":["https://openalex.org/W2165912799","https://openalex.org/W2965546495","https://openalex.org/W2735662278","https://openalex.org/W2382615723","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W4311804456","https://openalex.org/W1987484445","https://openalex.org/W3103844505","https://openalex.org/W2945311944"],"abstract_inverted_index":{"The":[0,67,93,114,135,151,173],"key":[1],"to":[2,15,29,44,83,122,126],"solving":[3],"fine-grained":[4,213],"image":[5],"categorization":[6,214],"is":[7,42,63,154],"finding":[8],"discriminate":[9,32],"and":[10,110,186,219],"local":[11],"regions":[12,168,190],"that":[13,40,142],"correspond":[14],"subtle":[16,46],"visual":[17,240],"traits.":[18],"Great":[19],"strides":[20],"have":[21],"been":[22],"made,":[23],"with":[24,71,233],"complex":[25],"networks":[26,205],"designed":[27],"specifically":[28],"learn":[30],"part-level":[31],"feature":[33,77,91,119,159],"representations.":[34],"In":[35],"this":[36],"paper,":[37],"we":[38,73],"show":[39,195],"it":[41,65],"possible":[43],"cultivate":[45],"details":[47],"without":[48,179],"the":[49,84,123,148,180,226,229],"need":[50,181],"for":[51,169,182,239],"overly":[52],"complicated":[53],"network":[54],"designs":[55],"or":[56],"training":[57],"mechanisms":[58],"-":[59],"a":[60,89,107,111,130,156,170],"single":[61],"loss":[62,95,100],"all":[64,118,211],"takes.":[66],"main":[68],"trick":[69],"lies":[70],"how":[72],"delve":[74],"into":[75],"individual":[76],"channels":[78,120,140],"early":[79],"on,":[80],"as":[81,98],"opposed":[82],"convention":[85],"of":[86,103,158,162,202,228],"starting":[87],"from":[88],"consolidated":[90],"map.":[92],"proposed":[94,236],"function,":[96],"termed":[97],"mutual-channel":[99],"(MC-Loss),":[101],"consists":[102],"two":[104,243],"channel-specific":[105],"components:":[106],"discriminality":[108,115],"component":[109,116,137],"diversity":[112,136],"component.":[113],"forces":[117],"belonging":[121],"same":[124],"class":[125],"be":[127,176],"discriminative,":[128],"through":[129],"novel":[131],"channel-wise":[132],"attention":[133],"mechanism.":[134],"additionally":[138],"constraints":[139],"so":[141],"they":[143],"become":[144],"mutually":[145],"exclusive":[146],"across":[147],"spatial":[149],"dimension.":[150],"end":[152],"result":[153],"therefore":[155],"set":[157],"channels,":[160],"each":[161],"which":[163],"reflects":[164],"different":[165,244],"locally":[166],"discriminative":[167,189],"specific":[171],"class.":[172],"MC-Loss":[174,197,230],"can":[175,206],"trained":[177],"end-to-end,":[178],"any":[183],"bounding-box/part":[184],"annotations,":[185],"yields":[187],"highly":[188],"during":[191],"inference.":[192],"Experimental":[193],"results":[194],"our":[196],"when":[198,231],"implemented":[199],"on":[200,210,242],"top":[201],"common":[203],"base":[204,245],"achieve":[207],"state-of-the-art":[208],"performance":[209],"four":[212],"datasets":[215],"(CUB-Birds,":[216],"FGVC-Aircraft,":[217],"Flowers-102,":[218],"Stanford":[220],"Cars).":[221],"Ablative":[222],"studies":[223],"further":[224],"demonstrate":[225],"superiority":[227],"compared":[232],"other":[234],"recently":[235],"general-purpose":[237],"losses":[238],"classification,":[241],"networks.":[246]},"counts_by_year":[{"year":2026,"cited_by_count":19},{"year":2025,"cited_by_count":50},{"year":2024,"cited_by_count":74},{"year":2023,"cited_by_count":79},{"year":2022,"cited_by_count":70},{"year":2021,"cited_by_count":82},{"year":2020,"cited_by_count":31}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}