{"id":"https://openalex.org/W3110861132","doi":"https://doi.org/10.1109/smc42975.2020.9283181","title":"Clothing Brand Logo Prediction: From Residual Block to Dense Block","display_name":"Clothing Brand Logo Prediction: From Residual Block to Dense Block","publication_year":2020,"publication_date":"2020-10-11","ids":{"openalex":"https://openalex.org/W3110861132","doi":"https://doi.org/10.1109/smc42975.2020.9283181","mag":"3110861132"},"language":"en","primary_location":{"id":"doi:10.1109/smc42975.2020.9283181","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc42975.2020.9283181","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","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/A5036481579","display_name":"Kuan-Hsien Liu","orcid":"https://orcid.org/0000-0002-1411-2113"},"institutions":[{"id":"https://openalex.org/I131948415","display_name":"National Taichung University of Science and Technology","ror":"https://ror.org/05bgcav40","country_code":"TW","type":"education","lineage":["https://openalex.org/I131948415"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Kuan-Hsien Liu","raw_affiliation_strings":["National Taichung University of Science and Technology, Taichung, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Taichung University of Science and Technology, Taichung, Taiwan","institution_ids":["https://openalex.org/I131948415"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022418382","display_name":"Tsung-Jung Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I162838928","display_name":"National Chung Hsing University","ror":"https://ror.org/05vn3ca78","country_code":"TW","type":"education","lineage":["https://openalex.org/I162838928"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Tsung-Jung Liu","raw_affiliation_strings":["National Chung Hsing University, Taichung, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Chung Hsing University, Taichung, Taiwan","institution_ids":["https://openalex.org/I162838928"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100455958","display_name":"Fei Wang","orcid":"https://orcid.org/0000-0003-3462-8472"},"institutions":[{"id":"https://openalex.org/I162838928","display_name":"National Chung Hsing University","ror":"https://ror.org/05vn3ca78","country_code":"TW","type":"education","lineage":["https://openalex.org/I162838928"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Fei Wang","raw_affiliation_strings":["National Chung Hsing University, Taichung, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Chung Hsing University, Taichung, Taiwan","institution_ids":["https://openalex.org/I162838928"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1665","last_page":"1670"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.989799976348877,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.989799976348877,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9801999926567078,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9775000214576721,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7814568281173706},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7802985906600952},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.75785231590271},{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.7479262351989746},{"id":"https://openalex.org/keywords/clothing","display_name":"Clothing","score":0.6672669053077698},{"id":"https://openalex.org/keywords/logo","display_name":"Logo (programming language)","score":0.6502753496170044},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6074521541595459},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5293629169464111},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.49844789505004883},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4545650780200958},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44547536969184875},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3727428913116455},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.355336993932724},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.11991089582443237},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1079740822315216},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09168392419815063}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7814568281173706},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7802985906600952},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.75785231590271},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.7479262351989746},{"id":"https://openalex.org/C530175646","wikidata":"https://www.wikidata.org/wiki/Q11460","display_name":"Clothing","level":2,"score":0.6672669053077698},{"id":"https://openalex.org/C2778720087","wikidata":"https://www.wikidata.org/wiki/Q201436","display_name":"Logo (programming language)","level":2,"score":0.6502753496170044},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6074521541595459},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5293629169464111},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.49844789505004883},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4545650780200958},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44547536969184875},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3727428913116455},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.355336993932724},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.11991089582443237},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1079740822315216},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09168392419815063},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc42975.2020.9283181","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc42975.2020.9283181","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W123584468","https://openalex.org/W146395692","https://openalex.org/W181871703","https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1567822572","https://openalex.org/W1686810756","https://openalex.org/W2047809566","https://openalex.org/W2091473544","https://openalex.org/W2135367695","https://openalex.org/W2143183660","https://openalex.org/W2157732827","https://openalex.org/W2163605009","https://openalex.org/W2164287382","https://openalex.org/W2170881581","https://openalex.org/W2171671655","https://openalex.org/W2194775991","https://openalex.org/W2344482072","https://openalex.org/W2410358280","https://openalex.org/W2471768434","https://openalex.org/W2613718673","https://openalex.org/W2767660127","https://openalex.org/W2769986739","https://openalex.org/W2796347433","https://openalex.org/W2891963524","https://openalex.org/W2912164984","https://openalex.org/W2940027585","https://openalex.org/W2943229868","https://openalex.org/W2943488693","https://openalex.org/W2943688177","https://openalex.org/W2946726474","https://openalex.org/W2954388973","https://openalex.org/W2963150697","https://openalex.org/W2963446712","https://openalex.org/W2964101377","https://openalex.org/W2965849643","https://openalex.org/W2968468089","https://openalex.org/W2970695947","https://openalex.org/W2971172296","https://openalex.org/W3006399382","https://openalex.org/W3006407587","https://openalex.org/W3043835189","https://openalex.org/W4293584584","https://openalex.org/W6605026522","https://openalex.org/W6607307712","https://openalex.org/W6620707391","https://openalex.org/W6631782140","https://openalex.org/W6637373629","https://openalex.org/W6684191040","https://openalex.org/W6687483927","https://openalex.org/W6750227808","https://openalex.org/W6762837317"],"related_works":["https://openalex.org/W4237171675","https://openalex.org/W3036286480","https://openalex.org/W4287027631","https://openalex.org/W3192357901","https://openalex.org/W2387360586","https://openalex.org/W2952736415","https://openalex.org/W3209723314","https://openalex.org/W3205398323","https://openalex.org/W2883297582","https://openalex.org/W4390524233"],"abstract_inverted_index":{"In":[0,52,123],"this":[1,53,101],"paper,":[2],"we":[3,37,55,126],"proposed":[4,105],"a":[5,15,73],"new":[6,74],"clothing":[7,63,76,86],"brand":[8,23,64,77,87,93],"prediction":[9],"method":[10],"which":[11],"is":[12],"rooted":[13],"on":[14],"dense-block":[16,58],"based":[17,59],"deep":[18,42],"convolutional":[19,30,43],"neural":[20,31,44],"network":[21],"for":[22,112],"logo":[24,65,88,96],"detection":[25,66],"and":[26,34,67,78,82,95,137],"recognition.":[27],"To":[28,103],"learn":[29],"networks":[32,45],"deeper":[33],"more":[35],"accurately,":[36],"adopted":[38],"dense":[39],"blocks":[40],"into":[41],"to":[46,61,98],"make":[47],"connections":[48],"between":[49],"layers":[50],"shorter.":[51],"work,":[54],"propose":[56],"several":[57,134],"designs":[60],"improve":[62],"recognition":[68],"accuracies.":[69],"We":[70],"also":[71],"constructed":[72],"large-scale":[75],"price":[79],"(CBP)":[80],"dataset":[81,90],"its":[83],"subset,":[84],"called":[85],"(CBL)":[89],"with":[91],"the":[92,113,120,124],"attribute":[94],"information":[97],"carry":[99],"out":[100],"task.":[102],"lower":[104],"framework":[106],"complexity,":[107],"two":[108],"pixel":[109],"search":[110,129],"steps":[111],"bounding":[114],"box":[115],"movement":[116],"are":[117],"implemented":[118],"in":[119],"training":[121],"procedure.":[122],"experiment,":[125],"show":[127],"our":[128],"reduced":[130],"model":[131],"can":[132],"outperform":[133],"state-of-the-art":[135],"methods":[136],"attain":[138],"good":[139],"performance.":[140]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}