{"id":"https://openalex.org/W2415072891","doi":"https://doi.org/10.1109/acpr.2015.7486460","title":"Enhancing RGB CNNs with depth","display_name":"Enhancing RGB CNNs with depth","publication_year":2015,"publication_date":"2015-11-01","ids":{"openalex":"https://openalex.org/W2415072891","doi":"https://doi.org/10.1109/acpr.2015.7486460","mag":"2415072891"},"language":"en","primary_location":{"id":"doi:10.1109/acpr.2015.7486460","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acpr.2015.7486460","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","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/A5082893662","display_name":"Arjun Sharma","orcid":"https://orcid.org/0000-0002-5705-5470"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Arjun Sharma","raw_affiliation_strings":["Xerox Research Centre, India"],"affiliations":[{"raw_affiliation_string":"Xerox Research Centre, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102818996","display_name":"K. Pramod Sankar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"K. Pramod Sankar","raw_affiliation_strings":["Xerox Research Centre, India"],"affiliations":[{"raw_affiliation_string":"Xerox Research Centre, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5082893662"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.21989213,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"11","issue":null,"first_page":"031","last_page":"035"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.9980000257492065,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9980000257492065,"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":0.9929999709129333,"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"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.992900013923645,"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/rgb-color-model","display_name":"RGB color model","score":0.8062381744384766},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7824586629867554},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7755559682846069},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7495993375778198},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6896502375602722},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6674047112464905},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.6346850395202637},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5610461831092834},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.5350277423858643},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.32535097002983093}],"concepts":[{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.8062381744384766},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7824586629867554},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7755559682846069},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7495993375778198},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6896502375602722},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6674047112464905},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.6346850395202637},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5610461831092834},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.5350277423858643},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.32535097002983093},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/acpr.2015.7486460","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acpr.2015.7486460","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","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":26,"referenced_works":["https://openalex.org/W1565402342","https://openalex.org/W1573897183","https://openalex.org/W1849277567","https://openalex.org/W2005756025","https://openalex.org/W2062118960","https://openalex.org/W2070961462","https://openalex.org/W2074142320","https://openalex.org/W2093102539","https://openalex.org/W2102605133","https://openalex.org/W2109992539","https://openalex.org/W2110798204","https://openalex.org/W2138857742","https://openalex.org/W2156222070","https://openalex.org/W2163605009","https://openalex.org/W2184188583","https://openalex.org/W2606321545","https://openalex.org/W2963038646","https://openalex.org/W6629098493","https://openalex.org/W6633727509","https://openalex.org/W6639204139","https://openalex.org/W6676481782","https://openalex.org/W6676766825","https://openalex.org/W6680300913","https://openalex.org/W6683562173","https://openalex.org/W6684191040","https://openalex.org/W6686207219"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W2745001401","https://openalex.org/W4321353415","https://openalex.org/W2185469136","https://openalex.org/W2130974462","https://openalex.org/W972276598","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W2087343574","https://openalex.org/W4246352526"],"abstract_inverted_index":{"Most":[0],"current":[1],"approaches":[2,61],"for":[3],"recognition":[4,117],"in":[5,9,115],"RGB-D":[6,127],"images":[7],"fall":[8],"either":[10],"the":[11,15,22,32,40,51],"late":[12,44,86],"fusion":[13,17,24,45,87],"or":[14],"early":[16,23,84],"category.":[18],"A":[19],"drawback":[20],"of":[21,31,46,54,88],"scheme":[25],"is":[26,34],"its":[27],"inapplicability":[28],"when":[29,93,103],"one":[30],"modalities":[33,55],"absent":[35],"at":[36],"test":[37],"time.":[38],"On":[39],"other":[41],"hand,":[42],"a":[43,77,112],"features":[47],"does":[48],"not":[49,66],"allow":[50],"correlated":[52],"nature":[53],"to":[56,68],"be":[57],"exploited":[58],"effectively.":[59],"Recent":[60],"using":[62],"Deep":[63],"Learning":[64],"are":[65,121],"immune":[67],"these":[69],"problems":[70],"either.":[71],"In":[72],"this":[73],"work,":[74],"we":[75],"propose":[76],"simple,":[78],"yet":[79],"elegant":[80],"method":[81],"towards":[82],"combining":[83],"and":[85,90],"colour":[89,116],"depth":[91,108],"information":[92],"training":[94],"deep":[95],"Convolutional":[96],"Neural":[97],"Networks":[98],"(CNNs).":[99],"We":[100],"show":[101],"that":[102],"fine-tuning":[104],"CNNs,":[105],"an":[106],"intermediate":[107],"pre-training":[109],"step":[110],"provides":[111],"significant":[113],"jump":[114],"accuracy.":[118],"The":[119],"trends":[120],"observed":[122],"consistently":[123],"over":[124],"several":[125],"benchmark":[126],"datasets.":[128]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
