{"id":"https://openalex.org/W2518372827","doi":"https://doi.org/10.1109/icip.2016.7532516","title":"Salient object detection via fast R-CNN and low-level cues","display_name":"Salient object detection via fast R-CNN and low-level cues","publication_year":2016,"publication_date":"2016-08-17","ids":{"openalex":"https://openalex.org/W2518372827","doi":"https://doi.org/10.1109/icip.2016.7532516","mag":"2518372827"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2016.7532516","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2016.7532516","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Image Processing (ICIP)","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/A5065663582","display_name":"Xiang Wang","orcid":"https://orcid.org/0000-0001-5900-8486"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiang Wang","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006236325","display_name":"Huimin Ma","orcid":"https://orcid.org/0000-0001-5383-5667"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huimin Ma","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089828993","display_name":"Xiaozhi Chen","orcid":"https://orcid.org/0009-0009-3064-6201"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaozhi Chen","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5065663582"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":2.7032,"has_fulltext":false,"cited_by_count":38,"citation_normalized_percentile":{"value":0.9369509,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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/T11094","display_name":"Face Recognition and Perception","score":0.9898999929428101,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9878000020980835,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/computer-science","display_name":"Computer science","score":0.7487096786499023},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.6615586876869202},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6073450446128845},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.582585871219635},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.547002375125885},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.46940553188323975},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3119361102581024}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7487096786499023},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.6615586876869202},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6073450446128845},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.582585871219635},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.547002375125885},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.46940553188323975},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3119361102581024}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2016.7532516","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2016.7532516","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Image Processing (ICIP)","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/W21025885","https://openalex.org/W845365781","https://openalex.org/W1536680647","https://openalex.org/W1581590495","https://openalex.org/W1686810756","https://openalex.org/W1772076007","https://openalex.org/W1947031653","https://openalex.org/W1965301399","https://openalex.org/W1982075130","https://openalex.org/W2002781701","https://openalex.org/W2020523103","https://openalex.org/W2036973297","https://openalex.org/W2037954058","https://openalex.org/W2038913936","https://openalex.org/W2039313011","https://openalex.org/W2047670868","https://openalex.org/W2100470808","https://openalex.org/W2124592697","https://openalex.org/W2128272608","https://openalex.org/W2129587342","https://openalex.org/W2131095668","https://openalex.org/W2131297486","https://openalex.org/W2147347517","https://openalex.org/W2155893237","https://openalex.org/W2157554677","https://openalex.org/W2161185676","https://openalex.org/W2211996548","https://openalex.org/W2216125271","https://openalex.org/W2270657321","https://openalex.org/W2962835968","https://openalex.org/W3104979525","https://openalex.org/W6600850345","https://openalex.org/W6637373629","https://openalex.org/W6640721603","https://openalex.org/W6659502896","https://openalex.org/W6660344663","https://openalex.org/W6681498701","https://openalex.org/W6688789216"],"related_works":["https://openalex.org/W2329500892","https://openalex.org/W28991112","https://openalex.org/W2370726991","https://openalex.org/W2369710579","https://openalex.org/W4327728159","https://openalex.org/W2912751582","https://openalex.org/W1990856605","https://openalex.org/W2053783616","https://openalex.org/W2545348020","https://openalex.org/W4292830139"],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2],"salient":[3],"object":[4],"detection":[5],"have":[6],"exploited":[7],"the":[8,21,58,65,105,115],"deep":[9],"Convolutional":[10],"Neural":[11],"Network":[12],"(CNN)":[13],"to":[14,20,33,103],"represent":[15],"high-level":[16,88],"semantic,":[17],"however,":[18],"due":[19],"presence":[22],"of":[23,54,98],"convolutional":[24],"and":[25,81,121],"pooling":[26],"layers,":[27],"it":[28],"is":[29,101],"difficult":[30],"for":[31],"CNN":[32],"generate":[34,51],"saliency":[35,52,106],"map":[36],"with":[37,69,87],"sharp":[38,70],"boundaries.":[39,71],"In":[40],"this":[41],"paper,":[42],"we":[43,76],"propose":[44,78],"multi-scale":[45],"mask-based":[46],"Fast":[47],"R-CNN":[48],"framework":[49],"which":[50,84,95],"score":[53],"each":[55],"region.":[56],"Since":[57],"regions":[59],"are":[60,67,85],"segmented":[61],"using":[62],"edge-preserved":[63],"methods,":[64],"results":[66],"naturally":[68],"To":[72],"consider":[73],"context":[74],"information,":[75],"also":[77],"low-level":[79],"contrast":[80],"backgroundness":[82],"prior":[83],"complementary":[86],"semantic.":[89],"Finally,":[90],"an":[91],"edge-based":[92],"propagation":[93],"method":[94,117],"takes":[96],"advantages":[97],"edge":[99],"information":[100],"proposed":[102,116],"refine":[104],"map.":[107],"Experiments":[108],"on":[109],"three":[110],"benchmark":[111],"datasets":[112],"demonstrate":[113],"that":[114],"outperforms":[118],"previous":[119],"methods":[120],"achieves":[122],"state-of-the-art":[123],"performance.":[124]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}