{"id":"https://openalex.org/W4385626877","doi":"https://doi.org/10.1109/tim.2023.3302911","title":"Novel Dilated Separable Convolution Networks for Efficient Video Salient Object Detection in the Wild","display_name":"Novel Dilated Separable Convolution Networks for Efficient Video Salient Object Detection in the Wild","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4385626877","doi":"https://doi.org/10.1109/tim.2023.3302911"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2023.3302911","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2023.3302911","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"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 Instrumentation and Measurement","raw_type":"journal-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/A5063067846","display_name":"Hemraj Singh","orcid":"https://orcid.org/0000-0002-7110-5147"},"institutions":[{"id":"https://openalex.org/I121750182","display_name":"National Institute of Technology Warangal","ror":"https://ror.org/017ebfz38","country_code":"IN","type":"education","lineage":["https://openalex.org/I121750182"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Hemraj Singh","raw_affiliation_strings":["Department of Computer Science and Engineering, National Institute of Technology, Warangal, Hanamkonda, Telangana, India"],"raw_orcid":"https://orcid.org/0000-0002-7110-5147","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, National Institute of Technology, Warangal, Hanamkonda, Telangana, India","institution_ids":["https://openalex.org/I121750182"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034834663","display_name":"Mridula Verma","orcid":"https://orcid.org/0000-0002-9772-240X"},"institutions":[{"id":"https://openalex.org/I150312865","display_name":"Institute for Development and Research in Banking Technology","ror":"https://ror.org/00ta0g865","country_code":"IN","type":"government","lineage":["https://openalex.org/I150312865"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Mridula Verma","raw_affiliation_strings":["Institute for Development and Research in Banking Technology, Hyderabad, Telangana, India"],"raw_orcid":"https://orcid.org/0000-0002-9772-240X","affiliations":[{"raw_affiliation_string":"Institute for Development and Research in Banking Technology, Hyderabad, Telangana, India","institution_ids":["https://openalex.org/I150312865"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082365220","display_name":"Ramalingaswamy Cheruku","orcid":"https://orcid.org/0000-0003-1677-5321"},"institutions":[{"id":"https://openalex.org/I121750182","display_name":"National Institute of Technology Warangal","ror":"https://ror.org/017ebfz38","country_code":"IN","type":"education","lineage":["https://openalex.org/I121750182"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ramalingaswamy Cheruku","raw_affiliation_strings":["Department of Computer Science and Engineering, National Institute of Technology, Warangal, Hanamkonda, Telangana, India"],"raw_orcid":"https://orcid.org/0000-0003-1677-5321","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, National Institute of Technology, Warangal, Hanamkonda, Telangana, India","institution_ids":["https://openalex.org/I121750182"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.021,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.88855881,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"72","issue":null,"first_page":"1","last_page":"13"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":1.0,"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":1.0,"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/T10971","display_name":"Olfactory and Sensory Function Studies","score":0.9876999855041504,"subfield":{"id":"https://openalex.org/subfields/2809","display_name":"Sensory Systems"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9815999865531921,"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":0.7008373737335205},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6180967092514038},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6154752373695374},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5748111009597778},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.5316455960273743},{"id":"https://openalex.org/keywords/dilation","display_name":"Dilation (metric space)","score":0.5000052452087402},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.43837472796440125},{"id":"https://openalex.org/keywords/clutter","display_name":"Clutter","score":0.41673892736434937},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2990102171897888},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.29418864846229553},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17276078462600708},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.13659366965293884}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7008373737335205},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6180967092514038},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6154752373695374},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5748111009597778},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.5316455960273743},{"id":"https://openalex.org/C2780757906","wikidata":"https://www.wikidata.org/wiki/Q5276676","display_name":"Dilation (metric space)","level":2,"score":0.5000052452087402},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.43837472796440125},{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.41673892736434937},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2990102171897888},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.29418864846229553},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17276078462600708},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.13659366965293884},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"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/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tim.2023.3302911","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2023.3302911","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"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 Instrumentation and Measurement","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":60,"referenced_works":["https://openalex.org/W2030994305","https://openalex.org/W2138682569","https://openalex.org/W2154943049","https://openalex.org/W2212077366","https://openalex.org/W2470139095","https://openalex.org/W2511458122","https://openalex.org/W2558027072","https://openalex.org/W2560474170","https://openalex.org/W2564998703","https://openalex.org/W2591696292","https://openalex.org/W2610147486","https://openalex.org/W2630837129","https://openalex.org/W2738760021","https://openalex.org/W2798823518","https://openalex.org/W2799157347","https://openalex.org/W2799239273","https://openalex.org/W2890853604","https://openalex.org/W2895340898","https://openalex.org/W2916797271","https://openalex.org/W2931853599","https://openalex.org/W2957408986","https://openalex.org/W2963149042","https://openalex.org/W2963548592","https://openalex.org/W2965638232","https://openalex.org/W2967199722","https://openalex.org/W2984144959","https://openalex.org/W2986056979","https://openalex.org/W2996803365","https://openalex.org/W2997217064","https://openalex.org/W2997487053","https://openalex.org/W2999458807","https://openalex.org/W3011978462","https://openalex.org/W3034320401","https://openalex.org/W3035487542","https://openalex.org/W3097337310","https://openalex.org/W3097815369","https://openalex.org/W3104844437","https://openalex.org/W3106773277","https://openalex.org/W3109908659","https://openalex.org/W3110030584","https://openalex.org/W3136838953","https://openalex.org/W3175841511","https://openalex.org/W3196248320","https://openalex.org/W3196444763","https://openalex.org/W3202285299","https://openalex.org/W3204643350","https://openalex.org/W3207101999","https://openalex.org/W4214542306","https://openalex.org/W4221142306","https://openalex.org/W4285058230","https://openalex.org/W4286370722","https://openalex.org/W4289598203","https://openalex.org/W4295312788","https://openalex.org/W4312526532","https://openalex.org/W4362654014","https://openalex.org/W6685670348","https://openalex.org/W6739696289","https://openalex.org/W6766978945","https://openalex.org/W6810081451","https://openalex.org/W6841310995"],"related_works":["https://openalex.org/W2357365693","https://openalex.org/W1550912305","https://openalex.org/W2032041146","https://openalex.org/W2141294180","https://openalex.org/W1489399123","https://openalex.org/W2408846072","https://openalex.org/W2922421953","https://openalex.org/W3002270006","https://openalex.org/W2079531124","https://openalex.org/W2161193411"],"abstract_inverted_index":{"Appearance":[0],"and":[1,21,88,95,123,133,155,168,176,197,246],"motion":[2,55,132,172],"are":[3,41,240],"essential":[4],"features":[5,20,135],"in":[6,45,98,199,225],"Video":[7],"Salient":[8],"Object":[9],"Detection":[10],"(VSOD)":[11],"tasks.":[12],"Most":[13],"of":[14,69,179,207,218,227,259,263,270],"the":[15,27,35,138,180,195,205,208,223,228,260,264,267,271,275,283],"existing":[16],"approaches":[17,64],"utilize":[18,161],"local":[19],"thus":[22],"fail":[23],"to":[24,43,73,81,89,128,160],"understand":[25],"both":[26],"appearance":[28,134,166],"as":[29,31,52,231,233],"well":[30,232],"motion-specific":[32],"semantics":[33],"at":[34],"global":[36],"level.":[37],"Hence,":[38],"these":[39,63,86],"methods":[40],"unable":[42],"perform":[44],"unconstrained":[46],"scenarios":[47],"where":[48],"multiple":[49],"challenges,":[50],"such":[51],"partial":[53],"occlusion,":[54],"blur,":[56],"noise,":[57],"clutter":[58],"background,":[59],"etc.,":[60],"exist.":[61],"Moreover,":[62],"require":[65],"a":[66,91,101,142,150,156,185,247],"large":[67],"number":[68],"computational":[70,96],"resources":[71],"due":[72],"their":[74,79],"complex":[75],"structures,":[76],"which":[77,109,192,220,280],"limits":[78],"applicability":[80],"real-world":[82,285],"deployment.":[83],"To":[84],"resolve":[85],"issues":[87],"achieve":[90],"balance":[92],"between":[93],"accuracy":[94],"complexity,":[97],"this":[99],"paper,":[100],"Dilation":[102,113],"Separable":[103,144,151],"Convolution":[104,145,152],"Network":[105,146],"(DSCNet)":[106],"is":[107,110,158,214,251,266],"proposed,":[108],"equipped":[111,148],"with":[112,149,253],"Attention":[114],"Fusion":[115,120],"Module":[116,121,126,153],"(DAFM),":[117],"Bi-directional":[118,143],"Cross-modality":[119],"(BCFM),":[122],"Saliency":[124],"Prediction":[125],"(SPM)":[127],"extract":[129],"enhanced":[130,170],"multi-scaled":[131,171],"without":[136],"increasing":[137],"model":[139,213],"complexity.":[140],"Further,":[141],"(BSC-Net)":[147],"(SCM)s":[154],"FlowNet2.0":[157],"proposed":[159,209,238,272],"multi-scale":[162],"contextual":[163],"information":[164],"across":[165],"cues":[167],"generate":[169],"maps.":[173],"For":[174],"faster":[175],"better":[177],"training":[178,229],"DSCNet":[181],"model,":[182],"we":[183],"propose":[184],"novel":[186],"Stochastic":[187],"Gradient-based":[188],"Firefly":[189],"Algorithm":[190],"(SGFA),":[191],"adaptively":[193],"balances":[194],"exploration":[196],"exploitation":[198],"multi-scaled,":[200],"cross-modal":[201],"embedded":[202],"sub-spaces.":[203],"With":[204],"help":[206],"SGFA":[210],"algorithm,":[211],"DSCNet+":[212],"constructed":[215],"on":[216,242,274],"top":[217],"DSCNet,":[219],"further":[221],"improves":[222],"results":[224],"terms":[226],"speed":[230],"other":[234],"evaluation":[235],"metrics.":[236],"The":[237],"models":[239,273],"evaluated":[241],"six":[243],"benchmark":[244],"datasets,":[245],"detailed":[248],"comparative":[249],"study":[250],"provided":[252],"sixteen":[254],"state-of-the-art":[255],"(SOTA)":[256],"models.":[257],"One":[258],"major":[261],"highlights":[262],"work":[265],"significant":[268],"performance":[269],"most":[276],"difficult":[277],"DAVSOD-Diff":[278],"dataset,":[279],"best":[281],"reflects":[282],"challenging":[284],"scenarios.":[286]},"counts_by_year":[{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":5}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
