{"id":"https://openalex.org/W3009736278","doi":"https://doi.org/10.1109/wacv45572.2020.9093354","title":"QUICKSAL: A small and sparse visual saliency model for efficient inference in resource constrained hardware","display_name":"QUICKSAL: A small and sparse visual saliency model for efficient inference in resource constrained hardware","publication_year":2020,"publication_date":"2020-03-01","ids":{"openalex":"https://openalex.org/W3009736278","doi":"https://doi.org/10.1109/wacv45572.2020.9093354","mag":"3009736278"},"language":"en","primary_location":{"id":"doi:10.1109/wacv45572.2020.9093354","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv45572.2020.9093354","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Winter Conference on Applications of Computer Vision (WACV)","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/A5076888060","display_name":"Vignesh Ramanathan","orcid":"https://orcid.org/0000-0002-0119-4420"},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Vignesh Ramanathan","raw_affiliation_strings":["Indian Institute of Science"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Science","institution_ids":["https://openalex.org/I59270414"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027754838","display_name":"Pritesh Dwivedi","orcid":"https://orcid.org/0009-0004-3153-2859"},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Pritesh Dwivedi","raw_affiliation_strings":["Indian Institute of Science"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Science","institution_ids":["https://openalex.org/I59270414"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032040262","display_name":"Bharath Katabathuni","orcid":null},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Bharath Katabathuni","raw_affiliation_strings":["Indian Institute of Science"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Science","institution_ids":["https://openalex.org/I59270414"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101611838","display_name":"Anirban Chakraborty","orcid":"https://orcid.org/0000-0002-6946-9152"},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Anirban Chakraborty","raw_affiliation_strings":["Indian Institute of Science"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Science","institution_ids":["https://openalex.org/I59270414"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085211717","display_name":"Chetan Singh Thakur","orcid":"https://orcid.org/0000-0002-1240-6214"},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Chetan Singh Thakur","raw_affiliation_strings":["Indian Institute of Science"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Science","institution_ids":["https://openalex.org/I59270414"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5076888060"],"corresponding_institution_ids":["https://openalex.org/I59270414"],"apc_list":null,"apc_paid":null,"fwci":0.0977,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.36059017,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"33","issue":null,"first_page":"1667","last_page":"1677"},"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9962999820709229,"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":0.9825999736785889,"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":0.7992477416992188},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.670350968837738},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6527913212776184},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.6364281177520752},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.624945342540741},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.6035973429679871},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5300174951553345},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4897441864013672},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4707435071468353},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4380718767642975},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.4278773367404938},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.32399117946624756},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08569592237472534}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7992477416992188},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.670350968837738},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6527913212776184},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.6364281177520752},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.624945342540741},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.6035973429679871},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5300174951553345},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4897441864013672},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4707435071468353},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4380718767642975},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.4278773367404938},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.32399117946624756},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08569592237472534},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv45572.2020.9093354","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv45572.2020.9093354","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Winter Conference on Applications of Computer Vision (WACV)","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":105,"referenced_works":["https://openalex.org/W566555209","https://openalex.org/W639708223","https://openalex.org/W845365781","https://openalex.org/W1581066146","https://openalex.org/W1686810756","https://openalex.org/W1690739335","https://openalex.org/W1745334888","https://openalex.org/W1773016195","https://openalex.org/W1821462560","https://openalex.org/W1894057436","https://openalex.org/W1897243830","https://openalex.org/W1923697677","https://openalex.org/W1942214758","https://openalex.org/W1946606198","https://openalex.org/W1947031653","https://openalex.org/W1948751323","https://openalex.org/W1948843088","https://openalex.org/W1965301399","https://openalex.org/W1994922096","https://openalex.org/W1996326832","https://openalex.org/W1999085092","https://openalex.org/W2002781701","https://openalex.org/W2037954058","https://openalex.org/W2039313011","https://openalex.org/W2078132377","https://openalex.org/W2086791339","https://openalex.org/W2091344378","https://openalex.org/W2097117768","https://openalex.org/W2108598243","https://openalex.org/W2119144962","https://openalex.org/W2128272608","https://openalex.org/W2134797427","https://openalex.org/W2163605009","https://openalex.org/W2167215970","https://openalex.org/W2194775991","https://openalex.org/W2221898772","https://openalex.org/W2293332611","https://openalex.org/W2300242332","https://openalex.org/W2314903836","https://openalex.org/W2319920447","https://openalex.org/W2402144811","https://openalex.org/W2437041077","https://openalex.org/W2461475918","https://openalex.org/W2472480899","https://openalex.org/W2519528544","https://openalex.org/W2553303224","https://openalex.org/W2613718673","https://openalex.org/W2744613561","https://openalex.org/W2757075404","https://openalex.org/W2780708736","https://openalex.org/W2788853733","https://openalex.org/W2805003733","https://openalex.org/W2899771611","https://openalex.org/W2943092876","https://openalex.org/W2950248853","https://openalex.org/W2953384591","https://openalex.org/W2962750597","https://openalex.org/W2962965870","https://openalex.org/W2962971773","https://openalex.org/W2963032190","https://openalex.org/W2963037989","https://openalex.org/W2963163009","https://openalex.org/W2963299740","https://openalex.org/W2963674932","https://openalex.org/W2963813662","https://openalex.org/W2963821229","https://openalex.org/W2963906836","https://openalex.org/W2964118293","https://openalex.org/W2964145162","https://openalex.org/W2964299589","https://openalex.org/W2964850420","https://openalex.org/W3025146914","https://openalex.org/W3098389804","https://openalex.org/W3106250896","https://openalex.org/W4239147634","https://openalex.org/W4288347538","https://openalex.org/W6615861906","https://openalex.org/W6620707391","https://openalex.org/W6634833660","https://openalex.org/W6637373629","https://openalex.org/W6637551013","https://openalex.org/W6638523607","https://openalex.org/W6639359414","https://openalex.org/W6639624585","https://openalex.org/W6640295612","https://openalex.org/W6640759395","https://openalex.org/W6640779376","https://openalex.org/W6649627412","https://openalex.org/W6677580257","https://openalex.org/W6679909955","https://openalex.org/W6684191040","https://openalex.org/W6684563725","https://openalex.org/W6698200048","https://openalex.org/W6700264148","https://openalex.org/W6713134421","https://openalex.org/W6726275242","https://openalex.org/W6729956949","https://openalex.org/W6746582238","https://openalex.org/W6747054740","https://openalex.org/W6748587240","https://openalex.org/W6751979845","https://openalex.org/W6756040250","https://openalex.org/W6762964460","https://openalex.org/W6784647857","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W4386603768","https://openalex.org/W2950475743","https://openalex.org/W2886711096","https://openalex.org/W2750384547","https://openalex.org/W4380078352","https://openalex.org/W3046591097","https://openalex.org/W4389249638","https://openalex.org/W2733410219","https://openalex.org/W2734358244","https://openalex.org/W4388700941"],"abstract_inverted_index":{"Visual":[0],"saliency":[1,108],"is":[2,46,57,154],"an":[3],"important":[4],"problem":[5],"in":[6,50,86],"the":[7,96,112,131,180],"field":[8],"of":[9,115,122],"cognitive":[10],"science":[11],"and":[12,25,35,52,63,77,171,182],"computer":[13],"vision":[14],"with":[15,98,119,130],"applications":[16],"such":[17],"as":[18],"surveillance,":[19],"adaptive":[20],"compressing,":[21],"detecting":[22],"unknown":[23],"objects":[24],"scene":[26],"understanding.":[27],"In":[28],"this":[29],"paper,":[30],"we":[31],"propose":[32],"a":[33],"small":[34],"sparse":[36],"neural":[37],"network":[38,103],"model":[39,56,118,123,175],"for":[40,48],"performing":[41],"salient":[42,181],"object":[43],"segmentation":[44],"that":[45,149,173],"suitable":[47],"use":[49],"mobile":[51],"embedded":[53],"applications.":[54],"Our":[55],"built":[58],"using":[59,82],"depthwise":[60],"separable":[61],"convolutions":[62],"bottleneck":[64],"inverted":[65],"residuals":[66,100],"which":[67],"have":[68],"been":[69],"proven":[70],"to":[71,104,128,138,156],"perform":[72],"very":[73],"memory":[74],"efficient":[75],"inference":[76],"can":[78,176],"be":[79],"easily":[80],"implemented":[81],"standard":[83],"functions":[84],"available":[85,144],"all":[87],"deep":[88,99],"learning":[89],"frameworks.":[90],"The":[91],"multiscale":[92],"features":[93],"extracted":[94],"along":[95],"layers":[97],"allow":[101],"our":[102,116,150,174],"learn":[105],"high":[106],"quality":[107],"maps.":[109],"We":[110,163],"present":[111,165],"quantitative":[113],"results":[114,167],"QUICKSAL":[117],"multiple":[120],"levels":[121],"sparsity":[124],"ranging":[125],"from":[126,136],"0%":[127],"~96%,":[129],"non-zero":[132],"parameter":[133],"count":[134,161],"varying":[135],"~3.3M":[137],"~0.14M":[139],"respectively":[140],"-":[141,147],"on":[142,168],"publicly":[143],"benchmark":[145],"datasets":[146],"showing":[148],"highly":[151],"constrained":[152],"approach":[153],"comparable":[155],"other":[157],"state-of-the-art":[158],"approaches":[159],"(parameter":[160],"~35M).":[162],"also":[164],"qualitative":[166],"camouflage":[169],"images":[170],"show":[172],"successfully":[177],"distinguish":[178],"between":[179],"non-salient":[183],"parts":[184],"even":[185],"when":[186],"both":[187],"seem":[188],"blended":[189],"together.":[190]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
