{"id":"https://openalex.org/W3160448557","doi":"https://doi.org/10.1109/icce50685.2021.9427582","title":"GAD: A Global Attraction Dataset and Attraction Classification Based on Residual Dense Convolutional Neural Networks","display_name":"GAD: A Global Attraction Dataset and Attraction Classification Based on Residual Dense Convolutional Neural Networks","publication_year":2021,"publication_date":"2021-01-10","ids":{"openalex":"https://openalex.org/W3160448557","doi":"https://doi.org/10.1109/icce50685.2021.9427582","mag":"3160448557"},"language":"en","primary_location":{"id":"doi:10.1109/icce50685.2021.9427582","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce50685.2021.9427582","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Consumer Electronics (ICCE)","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/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":true,"raw_author_name":"Kuan-Hsien Liu","raw_affiliation_strings":["National Taichung University of Science and Technology,Taiwan","National Taichung University of Science and Technology, Taiwan"],"affiliations":[{"raw_affiliation_string":"National Taichung University of Science and Technology,Taiwan","institution_ids":["https://openalex.org/I131948415"]},{"raw_affiliation_string":"National Taichung University of Science and Technology, Taiwan","institution_ids":["https://openalex.org/I131948415"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110607896","display_name":"Yu-Chen Lai","orcid":null},"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":"Yu-Chen Lai","raw_affiliation_strings":["National Taichung University of Science and Technology,Taiwan","National Taichung University of Science and Technology, Taiwan"],"affiliations":[{"raw_affiliation_string":"National Taichung University of Science and Technology,Taiwan","institution_ids":["https://openalex.org/I131948415"]},{"raw_affiliation_string":"National Taichung University of Science and Technology, Taiwan","institution_ids":["https://openalex.org/I131948415"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023526360","display_name":"Tsung-Jung Liu","orcid":"https://orcid.org/0000-0003-4296-0942"},"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"]},{"id":"https://openalex.org/I99613584","display_name":"National Taipei University","ror":"https://ror.org/03e29r284","country_code":"TW","type":"education","lineage":["https://openalex.org/I99613584"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Tsung-Jung Liu","raw_affiliation_strings":["National Chung Hsing University,Taiwan","National Chung Hsing University, Taiwan"],"affiliations":[{"raw_affiliation_string":"National Chung Hsing University,Taiwan","institution_ids":["https://openalex.org/I99613584","https://openalex.org/I162838928"]},{"raw_affiliation_string":"National Chung Hsing University, Taiwan","institution_ids":["https://openalex.org/I99613584","https://openalex.org/I162838928"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5036481579"],"corresponding_institution_ids":["https://openalex.org/I131948415"],"apc_list":null,"apc_paid":null,"fwci":0.0961,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.36037582,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9987999796867371,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9987999796867371,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.998199999332428,"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/T11439","display_name":"Video Analysis and Summarization","score":0.9973000288009644,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8058944344520569},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.793745219707489},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7892245054244995},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.7788679599761963},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7206851243972778},{"id":"https://openalex.org/keywords/attraction","display_name":"Attraction","score":0.7037830948829651},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5116596221923828},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.49082431197166443},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.4316957890987396},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.42139533162117004},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4151780903339386},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.11541756987571716},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11331379413604736},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07509246468544006}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8058944344520569},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.793745219707489},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7892245054244995},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.7788679599761963},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7206851243972778},{"id":"https://openalex.org/C2778364177","wikidata":"https://www.wikidata.org/wiki/Q2247133","display_name":"Attraction","level":2,"score":0.7037830948829651},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5116596221923828},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49082431197166443},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.4316957890987396},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.42139533162117004},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4151780903339386},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.11541756987571716},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11331379413604736},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07509246468544006},{"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/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"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/icce50685.2021.9427582","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce50685.2021.9427582","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Consumer Electronics (ICCE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1567822572","https://openalex.org/W1924160326","https://openalex.org/W2017814585","https://openalex.org/W2163605009","https://openalex.org/W2164287382","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2344482072","https://openalex.org/W2417429787","https://openalex.org/W2475223347","https://openalex.org/W2531409750","https://openalex.org/W2612445135","https://openalex.org/W2732026016","https://openalex.org/W2755125693","https://openalex.org/W2767660127","https://openalex.org/W2796347433","https://openalex.org/W2801295340","https://openalex.org/W2912164984","https://openalex.org/W2940027585","https://openalex.org/W2943229868","https://openalex.org/W2943488693","https://openalex.org/W2946726474","https://openalex.org/W2963446712","https://openalex.org/W2964121744","https://openalex.org/W2968468089","https://openalex.org/W2970695947","https://openalex.org/W2971172296","https://openalex.org/W3006407587","https://openalex.org/W3104752576","https://openalex.org/W4293584584","https://openalex.org/W4297775537","https://openalex.org/W6631190155","https://openalex.org/W6684191040","https://openalex.org/W6725739302","https://openalex.org/W6728184133","https://openalex.org/W6737664043","https://openalex.org/W6750227808"],"related_works":["https://openalex.org/W4386786398","https://openalex.org/W2138293586","https://openalex.org/W2080052720","https://openalex.org/W4319783074","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Over":[0],"the":[1,4,73],"past":[2],"years,":[3],"development":[5],"of":[6],"big":[7],"data":[8,14],"have":[9,22],"received":[10],"great":[11],"attention,":[12],"and":[13],"growth":[15],"continues":[16],"to":[17,43,51,105],"increase.":[18],"Deep":[19],"learning":[20,38],"technologies":[21],"shown":[23],"good":[24],"performance":[25,108],"in":[26,54],"many":[27],"practical":[28],"applications.":[29],"In":[30],"this":[31],"work,":[32],"we":[33,58,79],"proposed":[34,101],"a":[35,60,81,87,94],"new":[36,61,82],"deep":[37,112,129],"model":[39],"with":[40,45],"low":[41],"complexity":[42],"deal":[44],"global":[46,63],"attraction":[47,64,83],"classification":[48,84],"problem.":[49],"Due":[50],"limited":[52],"datasets":[53],"outdoor":[55],"scenes,":[56],"firstly,":[57],"constructed":[59,119],"large-scale":[62],"dataset":[65],"(GAD),":[66],"containing":[67],"more":[68],"than":[69,109],"380k":[70],"images":[71],"for":[72],"world's":[74],"250":[75],"famous":[76],"attractions.":[77],"Then":[78],"propose":[80],"method,":[85],"where":[86],"residual":[88,131],"dense":[89,132],"module":[90],"is":[91,103],"integrated":[92],"into":[93],"low-complexity":[95],"convolutional":[96,113,126],"neural":[97,114,127],"network":[98,115],"model.":[99],"Our":[100],"method":[102],"demonstrated":[104],"show":[106],"better":[107],"several":[110],"state-of-the-art":[111],"models":[116],"on":[117],"our":[118],"GAD":[120],"dataset.":[121],"Index":[122],"Terms-Artificial":[123],"intelligence,":[124],"classification,":[125],"network,":[128],"learning,":[130],"block.":[133]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}