{"id":"https://openalex.org/W2736551994","doi":"https://doi.org/10.1109/icra.2017.7989166","title":"Self-paced cross-modality transfer learning for efficient road segmentation","display_name":"Self-paced cross-modality transfer learning for efficient road segmentation","publication_year":2017,"publication_date":"2017-05-01","ids":{"openalex":"https://openalex.org/W2736551994","doi":"https://doi.org/10.1109/icra.2017.7989166","mag":"2736551994"},"language":"en","primary_location":{"id":"doi:10.1109/icra.2017.7989166","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2017.7989166","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Robotics and Automation (ICRA)","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/A5102747545","display_name":"Weiyue Wang","orcid":"https://orcid.org/0000-0002-8114-8271"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Weiyue Wang","raw_affiliation_strings":["Laboratory of Computer Science department, University of Southern California, CA, USA"],"affiliations":[{"raw_affiliation_string":"Laboratory of Computer Science department, University of Southern California, CA, USA","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100742122","display_name":"Naiyan Wang","orcid":"https://orcid.org/0000-0002-0526-3331"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Naiyan Wang","raw_affiliation_strings":["TuSimple"],"affiliations":[{"raw_affiliation_string":"TuSimple","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062971939","display_name":"Xiaomin Wu","orcid":"https://orcid.org/0000-0002-0898-4185"},"institutions":[{"id":"https://openalex.org/I4210162190","display_name":"China University of Petroleum, East China","ror":"https://ror.org/05gbn2817","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210162190"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaomin Wu","raw_affiliation_strings":["Department of Electrical Engineering, University of Petroleum (East China), Shandong, China"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of Petroleum (East China), Shandong, China","institution_ids":["https://openalex.org/I4210162190"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101137673","display_name":"Suya You","orcid":null},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Suya You","raw_affiliation_strings":["Laboratory of Computer Science department, University of Southern California, CA, USA"],"affiliations":[{"raw_affiliation_string":"Laboratory of Computer Science department, University of Southern California, CA, USA","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082235583","display_name":"Ulrich Neumann","orcid":"https://orcid.org/0000-0001-8977-7112"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ulrich Neumann","raw_affiliation_strings":["Laboratory of Computer Science department, University of Southern California, CA, USA"],"affiliations":[{"raw_affiliation_string":"Laboratory of Computer Science department, University of Southern California, CA, USA","institution_ids":["https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5102747545"],"corresponding_institution_ids":["https://openalex.org/I1174212"],"apc_list":null,"apc_paid":null,"fwci":0.9102,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.83725835,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1394","last_page":"1401"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"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":0.8568589687347412},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7695393562316895},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7524087429046631},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.7413191795349121},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7411666512489319},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.7393356561660767},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6294341087341309},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.5521447062492371},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5041018724441528},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5002036094665527},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.45781320333480835},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.42307180166244507},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41685065627098083},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4156584143638611},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.06583189964294434}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8568589687347412},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7695393562316895},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7524087429046631},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.7413191795349121},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7411666512489319},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.7393356561660767},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6294341087341309},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.5521447062492371},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5041018724441528},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5002036094665527},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.45781320333480835},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.42307180166244507},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41685065627098083},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4156584143638611},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.06583189964294434},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"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/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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icra.2017.7989166","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2017.7989166","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.6399999856948853,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":73,"referenced_works":["https://openalex.org/W33116912","https://openalex.org/W1677182931","https://openalex.org/W1686810756","https://openalex.org/W1745334888","https://openalex.org/W1795068553","https://openalex.org/W1836465849","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1910657905","https://openalex.org/W1913356549","https://openalex.org/W1923697677","https://openalex.org/W1924453835","https://openalex.org/W1948751323","https://openalex.org/W1964608713","https://openalex.org/W1995137594","https://openalex.org/W2028606404","https://openalex.org/W2037227137","https://openalex.org/W2045724293","https://openalex.org/W2047166686","https://openalex.org/W2100913937","https://openalex.org/W2108598243","https://openalex.org/W2109815793","https://openalex.org/W2121955477","https://openalex.org/W2124592697","https://openalex.org/W2132984949","https://openalex.org/W2133434696","https://openalex.org/W2138361487","https://openalex.org/W2160445453","https://openalex.org/W2161337244","https://openalex.org/W2167222293","https://openalex.org/W2168705645","https://openalex.org/W2172612767","https://openalex.org/W2186615578","https://openalex.org/W2194775991","https://openalex.org/W2259424905","https://openalex.org/W2302255633","https://openalex.org/W2332618872","https://openalex.org/W2338103624","https://openalex.org/W2340897893","https://openalex.org/W2413473068","https://openalex.org/W2419448466","https://openalex.org/W2492904644","https://openalex.org/W2494236530","https://openalex.org/W2511969587","https://openalex.org/W2962835968","https://openalex.org/W2963422987","https://openalex.org/W2963505902","https://openalex.org/W2963840672","https://openalex.org/W3100388886","https://openalex.org/W4293402673","https://openalex.org/W4293406525","https://openalex.org/W6601344910","https://openalex.org/W6637373629","https://openalex.org/W6638264490","https://openalex.org/W6638667902","https://openalex.org/W6639780620","https://openalex.org/W6639824700","https://openalex.org/W6639824712","https://openalex.org/W6640249263","https://openalex.org/W6640295612","https://openalex.org/W6675038308","https://openalex.org/W6676297131","https://openalex.org/W6679390333","https://openalex.org/W6679805309","https://openalex.org/W6680556580","https://openalex.org/W6685365618","https://openalex.org/W6686509673","https://openalex.org/W6696085341","https://openalex.org/W6698183232","https://openalex.org/W6702622491","https://openalex.org/W6703425876","https://openalex.org/W6717372056","https://openalex.org/W6723543151"],"related_works":["https://openalex.org/W2385859805","https://openalex.org/W2530972254","https://openalex.org/W3183901164","https://openalex.org/W2951211570","https://openalex.org/W3135818718","https://openalex.org/W4290188444","https://openalex.org/W3003905048","https://openalex.org/W2253429366","https://openalex.org/W3127975138","https://openalex.org/W4295520087"],"abstract_inverted_index":{"Accurate":[0],"road":[1,142],"segmentation":[2,128],"is":[3,24],"a":[4,44,66,107,152],"prerequisite":[5],"for":[6],"autonomous":[7],"driving.":[8],"Current":[9],"state-of-the-art":[10],"methods":[11,150],"are":[12],"mostly":[13],"based":[14],"on":[15,140],"convolutional":[16],"neural":[17],"networks":[18],"(CNNs).":[19],"Nevertheless,":[20],"their":[21],"good":[22,113],"performance":[23],"at":[25,151],"expense":[26],"of":[27,60,154],"abundant":[28],"annotated":[29,109],"data":[30],"and":[31],"high":[32],"computational":[33],"cost.":[34,133],"In":[35,115],"this":[36],"work,":[37],"we":[38,63,80,99,117,136],"address":[39],"these":[40,82],"two":[41],"issues":[42],"by":[43],"self-paced":[45,95],"cross-modality":[46],"transfer":[47,81],"learning":[48],"framework":[49],"with":[50,57,74,94,106,130],"efficient":[51,120],"projection":[52,121],"CNN.":[53],"To":[54],"be":[55],"specific,":[56],"the":[58,104,126],"help":[59],"stereo":[61],"images,":[62],"first":[64],"tackle":[65],"relevant":[67],"but":[68,84],"easier":[69],"task,":[70],"i.e.":[71],"free-space":[72],"detection":[73],"well":[75],"developed":[76],"unsupervised":[77],"methods.":[78],"Then,":[79],"useful":[83],"noisy":[85],"knowledge":[86],"in":[87],"depth":[88],"modality":[89,93],"to":[90,102,111],"single":[91],"RGB":[92],"CNN":[96,105],"learning.":[97],"Finally,":[98],"only":[100],"need":[101],"fine-tune":[103],"few":[108],"images":[110],"get":[112],"performance.":[114],"addition,":[116],"propose":[118],"an":[119],"CNN,":[122],"which":[123],"can":[124],"improve":[125],"fine-grained":[127],"results":[129],"little":[131],"additional":[132],"At":[134],"last,":[135],"test":[137],"our":[138],"method":[139,146],"KITTI":[141],"benchmark.":[143],"Our":[144],"proposed":[145],"surpasses":[147],"all":[148],"published":[149],"speed":[153],"15fps.":[155]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
