{"id":"https://openalex.org/W4386598504","doi":"https://doi.org/10.1109/icip49359.2023.10222029","title":"Multi-Task Model Based on Vision Task Level for Saliency Object Detection in Foggy Conditions","display_name":"Multi-Task Model Based on Vision Task Level for Saliency Object Detection in Foggy Conditions","publication_year":2023,"publication_date":"2023-09-11","ids":{"openalex":"https://openalex.org/W4386598504","doi":"https://doi.org/10.1109/icip49359.2023.10222029"},"language":"en","primary_location":{"id":"doi:10.1109/icip49359.2023.10222029","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip49359.2023.10222029","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Image Processing (ICIP)","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/A5101590138","display_name":"Yusen Zhu","orcid":"https://orcid.org/0009-0005-8286-6282"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yusen Zhu","raw_affiliation_strings":["Xinjiang University,Key Laboratory of Signal Detection and Processing,Urumqi,China,830046"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xinjiang University,Key Laboratory of Signal Detection and Processing,Urumqi,China,830046","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054814122","display_name":"Gang Zhou","orcid":"https://orcid.org/0000-0002-4425-9837"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gang Zhou","raw_affiliation_strings":["Xinjiang University,Key Laboratory of Signal Detection and Processing,Urumqi,China,830046"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xinjiang University,Key Laboratory of Signal Detection and Processing,Urumqi,China,830046","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111037941","display_name":"Jingxu Ren","orcid":null},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingxu Ren","raw_affiliation_strings":["Xinjiang University,Key Laboratory of Signal Detection and Processing,Urumqi,China,830046"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xinjiang University,Key Laboratory of Signal Detection and Processing,Urumqi,China,830046","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114140522","display_name":"Jiakun Tian","orcid":null},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiakun Tian","raw_affiliation_strings":["Xinjiang University,Key Laboratory of Signal Detection and Processing,Urumqi,China,830046"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xinjiang University,Key Laboratory of Signal Detection and Processing,Urumqi,China,830046","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100684815","display_name":"Zhenhong Jia","orcid":"https://orcid.org/0000-0001-6671-0206"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenhong Jia","raw_affiliation_strings":["Xinjiang University,Key Laboratory of Signal Detection and Processing,Urumqi,China,830046"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xinjiang University,Key Laboratory of Signal Detection and Processing,Urumqi,China,830046","institution_ids":["https://openalex.org/I96908189"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I96908189"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3055","last_page":"3059"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9998999834060669,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9998999834060669,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9983999729156494,"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":0.9929999709129333,"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":0.8505797982215881},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.8162953853607178},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.8139840960502625},{"id":"https://openalex.org/keywords/visibility","display_name":"Visibility","score":0.7837674617767334},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7224199175834656},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.7013028860092163},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6623649001121521},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.654190182685852},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.47491133213043213},{"id":"https://openalex.org/keywords/saliency-map","display_name":"Saliency map","score":0.41375333070755005},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41302645206451416}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8505797982215881},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8162953853607178},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.8139840960502625},{"id":"https://openalex.org/C123403432","wikidata":"https://www.wikidata.org/wiki/Q654068","display_name":"Visibility","level":2,"score":0.7837674617767334},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7224199175834656},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7013028860092163},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6623649001121521},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.654190182685852},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.47491133213043213},{"id":"https://openalex.org/C2779679900","wikidata":"https://www.wikidata.org/wiki/Q25304431","display_name":"Saliency map","level":3,"score":0.41375333070755005},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41302645206451416},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","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":1,"locations":[{"id":"doi:10.1109/icip49359.2023.10222029","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip49359.2023.10222029","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.6299999952316284}],"awards":[{"id":"https://openalex.org/G2414894066","display_name":null,"funder_award_id":"62166040","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3705503390","display_name":null,"funder_award_id":"2021D01C057","funder_id":"https://openalex.org/F4320328898","funder_display_name":"Natural Science Foundation of Xinjiang"},{"id":"https://openalex.org/G4896784468","display_name":null,"funder_award_id":"62137002","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7183778438","display_name":null,"funder_award_id":"62261053","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"},{"id":"https://openalex.org/F4320328898","display_name":"Natural Science Foundation of Xinjiang","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1514535095","https://openalex.org/W1854404533","https://openalex.org/W2068078373","https://openalex.org/W2086866337","https://openalex.org/W2792829624","https://openalex.org/W2939217524","https://openalex.org/W2959581809","https://openalex.org/W2961348656","https://openalex.org/W2963591054","https://openalex.org/W2963928582","https://openalex.org/W2990984982","https://openalex.org/W2997316506","https://openalex.org/W2998449272","https://openalex.org/W3009058813","https://openalex.org/W3009702330","https://openalex.org/W3025800305","https://openalex.org/W3034185160","https://openalex.org/W3173370190","https://openalex.org/W4226191818","https://openalex.org/W6630875275","https://openalex.org/W6638992375","https://openalex.org/W6766759761"],"related_works":["https://openalex.org/W2392812199","https://openalex.org/W4200176076","https://openalex.org/W2969228573","https://openalex.org/W2963690996","https://openalex.org/W4292830139","https://openalex.org/W4319309705","https://openalex.org/W2666741004","https://openalex.org/W3121027642","https://openalex.org/W1993208111","https://openalex.org/W3179866064"],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"saliency":[3,37,74,97,121],"object":[4,38,75,98,122],"detection":[5,39,76,123],"methods":[6,40],"based":[7],"on":[8,109],"convolutional":[9],"neural":[10],"networks":[11],"have":[12,17],"been":[13,106],"widely":[14],"studied,":[15],"and":[16,66,114],"achieved":[18],"excellent":[19],"performance":[20,95],"in":[21,32,77],"clear":[22],"images.":[23],"However,":[24],"due":[25],"to":[26,72,81,92],"the":[27,35,94],"low":[28],"visibility":[29],"of":[30,84,96],"images":[31],"foggy":[33,78,112,116],"conditions,":[34],"existing":[36],"will":[41],"be":[42],"seriously":[43],"affected":[44],"or":[45],"even":[46],"ineffective.":[47],"To":[48],"address":[49],"this":[50],"problem,":[51],"we":[52],"introduce":[53],"an":[54],"end-to-end":[55],"multi-task":[56],"learning":[57],"network.":[58],"We":[59],"design":[60],"two":[61],"subetworks":[62],"for":[63],"depth":[64],"estimation":[65],"image":[67],"restoration":[68],"as":[69],"auxiliary":[70],"tasks":[71],"improve":[73,93],"conditions.":[79],"According":[80],"different":[82,87],"characteristics":[83],"vision":[85],"tasks,":[86],"shared":[88],"layers":[89],"are":[90],"assigned":[91],"detection.":[99],"Experiments":[100],"show":[101],"that":[102],"our":[103],"method":[104],"has":[105],"greatly":[107],"improved":[108],"both":[110],"synthetic":[111],"datasets":[113],"real-to-world":[115],"datasets,":[117],"outperforming":[118],"many":[119],"state-to-the-art":[120],"methods.":[124]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
