{"id":"https://openalex.org/W4385565385","doi":"https://doi.org/10.1145/3580305.3599360","title":"Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning","display_name":"Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning","publication_year":2023,"publication_date":"2023-08-04","ids":{"openalex":"https://openalex.org/W4385565385","doi":"https://doi.org/10.1145/3580305.3599360"},"language":"en","primary_location":{"id":"doi:10.1145/3580305.3599360","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3580305.3599360","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599360","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599360","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5023273288","display_name":"Xin Qin","orcid":"https://orcid.org/0000-0002-4717-4310"},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Qin","raw_affiliation_strings":["Beijing Key Lab. of Mobile Com., CAS, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-4717-4310","affiliations":[{"raw_affiliation_string":"Beijing Key Lab. of Mobile Com., CAS, Beijing, China","institution_ids":["https://openalex.org/I180662265"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100700956","display_name":"Jindong Wang","orcid":"https://orcid.org/0000-0002-4833-0880"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jindong Wang","raw_affiliation_strings":["Microsoft Research Asia, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-4833-0880","affiliations":[{"raw_affiliation_string":"Microsoft Research Asia, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028911623","display_name":"Shuo Ma","orcid":"https://orcid.org/0000-0001-8896-0070"},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuo Ma","raw_affiliation_strings":["Beijing Key Lab. of Mobile Com., CAS, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-8896-0070","affiliations":[{"raw_affiliation_string":"Beijing Key Lab. of Mobile Com., CAS, Beijing, China","institution_ids":["https://openalex.org/I180662265"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100774978","display_name":"Lu Wang","orcid":"https://orcid.org/0000-0003-4035-0737"},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wang Lu","raw_affiliation_strings":["Beijing Key Lab. of Mobile Com., CAS, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-4035-0737","affiliations":[{"raw_affiliation_string":"Beijing Key Lab. of Mobile Com., CAS, Beijing, China","institution_ids":["https://openalex.org/I180662265"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101880191","display_name":"Yongchun Zhu","orcid":"https://orcid.org/0000-0002-7096-3190"},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongchun Zhu","raw_affiliation_strings":["Beijing Key Lab. of Mobile Com., CAS, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-7096-3190","affiliations":[{"raw_affiliation_string":"Beijing Key Lab. of Mobile Com., CAS, Beijing, China","institution_ids":["https://openalex.org/I180662265"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044651577","display_name":"Xing Xie","orcid":"https://orcid.org/0000-0002-8608-8482"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xing Xie","raw_affiliation_strings":["Microsoft Research Asia, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-8608-8482","affiliations":[{"raw_affiliation_string":"Microsoft Research Asia, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008558592","display_name":"Yiqiang Chen","orcid":"https://orcid.org/0000-0002-8407-0780"},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiqiang Chen","raw_affiliation_strings":["Beijing Key Lab. of Mobile Com., CAS, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-8407-0780","affiliations":[{"raw_affiliation_string":"Beijing Key Lab. of Mobile Com., CAS, Beijing, China","institution_ids":["https://openalex.org/I180662265"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.7901,"has_fulltext":true,"cited_by_count":23,"citation_normalized_percentile":{"value":0.93549835,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1943","last_page":"1953"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9998999834060669,"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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9998999834060669,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9905999898910522,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9865000247955322,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/discriminative-model","display_name":"Discriminative model","score":0.836972177028656},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7407397627830505},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6901295185089111},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6537579298019409},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.5774990916252136},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.525574266910553},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.5159812569618225},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.47840628027915955},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.47040635347366333},{"id":"https://openalex.org/keywords/resource","display_name":"Resource (disambiguation)","score":0.44004201889038086},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.415092408657074},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.084128737449646}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.836972177028656},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7407397627830505},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6901295185089111},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6537579298019409},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.5774990916252136},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.525574266910553},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.5159812569618225},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.47840628027915955},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.47040635347366333},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.44004201889038086},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.415092408657074},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.084128737449646},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"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/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3580305.3599360","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3580305.3599360","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599360","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3580305.3599360","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3580305.3599360","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599360","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.6499999761581421,"id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G5033718275","display_name":null,"funder_award_id":"XDA28040500","funder_id":"https://openalex.org/F4320321133","funder_display_name":"Chinese Academy of Sciences"},{"id":"https://openalex.org/G8567002948","display_name":"\u9762\u5411\u5e15\u91d1\u68ee\u65e9\u671f\u9884\u8b66\u7684\u8054\u90a6\u8fc1\u79fb\u5b66\u4e60\u65b9\u6cd5\u7814\u7a76","funder_award_id":"61972383","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321133","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4385565385.pdf","grobid_xml":"https://content.openalex.org/works/W4385565385.grobid-xml"},"referenced_works_count":43,"referenced_works":["https://openalex.org/W297279189","https://openalex.org/W569478347","https://openalex.org/W1857382374","https://openalex.org/W2023302299","https://openalex.org/W2026297770","https://openalex.org/W2144348409","https://openalex.org/W2145343602","https://openalex.org/W2187089797","https://openalex.org/W2348797658","https://openalex.org/W2620664872","https://openalex.org/W2758584002","https://openalex.org/W2786808285","https://openalex.org/W2896783937","https://openalex.org/W2958360136","https://openalex.org/W2963043696","https://openalex.org/W2981540341","https://openalex.org/W2997310315","https://openalex.org/W3003860764","https://openalex.org/W3011304563","https://openalex.org/W3018464563","https://openalex.org/W3034373371","https://openalex.org/W3038585207","https://openalex.org/W3044326989","https://openalex.org/W3080108946","https://openalex.org/W3099025572","https://openalex.org/W3101667008","https://openalex.org/W3127512060","https://openalex.org/W3153149826","https://openalex.org/W3173151551","https://openalex.org/W3174812227","https://openalex.org/W3189951784","https://openalex.org/W3195659354","https://openalex.org/W3200371321","https://openalex.org/W3201074629","https://openalex.org/W4214480085","https://openalex.org/W4220693285","https://openalex.org/W4224931233","https://openalex.org/W4282979157","https://openalex.org/W4287812705","https://openalex.org/W4289517877","https://openalex.org/W6601630192","https://openalex.org/W6688325169","https://openalex.org/W6776700526"],"related_works":["https://openalex.org/W4287995534","https://openalex.org/W2998168123","https://openalex.org/W2012451022","https://openalex.org/W2308097916","https://openalex.org/W4281386417","https://openalex.org/W2580898087","https://openalex.org/W3025754292","https://openalex.org/W3111031095","https://openalex.org/W2061273563","https://openalex.org/W1669299330"],"abstract_inverted_index":{"Human":[0],"activity":[1,142],"recognition":[2],"(HAR)":[3],"is":[4,23,46,220],"a":[5,26,31,106,147,213],"time":[6,49],"series":[7],"classification":[8],"task":[9],"that":[10,191],"focuses":[11],"on":[12,185],"identifying":[13],"the":[14,60,94,128,135,140,153,160,164,206],"motion":[15],"patterns":[16],"from":[17,66],"human":[18],"sensor":[19],"readings.":[20],"Adequate":[21],"data":[22,56,64,136],"essential":[24],"but":[25],"major":[27],"bottleneck":[28],"for":[29,116],"training":[30],"generalizable":[32,117],"HAR":[33,91,188],"model,":[34],"which":[35],"assists":[36],"customization":[37],"and":[38,50,84,111,124,138,166,216],"optimization":[39],"of":[40,155,203],"online":[41],"web":[42],"applications.":[43],"However,":[44],"it":[45],"costly":[47],"in":[48,57],"economy":[51],"to":[52,73,90,97,151],"collect":[53],"large-scale":[54],"labeled":[55],"reality,":[58],"i.e.,":[59],"low-resource":[61,83,118,207],"challenge.":[62],"Meanwhile,":[63,169],"collected":[65],"different":[67,74],"persons":[68],"have":[69],"distribution":[70,85,161,208],"shifts":[71],"due":[72],"living":[75],"habits,":[76],"body":[77],"shapes,":[78],"age":[79],"groups,":[80],"etc.":[81],"The":[82],"shift":[86,209],"challenges":[87],"are":[88],"detrimental":[89],"when":[92],"applying":[93],"trained":[95],"model":[96],"new":[98],"unseen":[99],"subjects.":[100],"In":[101],"this":[102],"paper,":[103],"we":[104,145],"propose":[105,146],"novel":[107],"approach":[108],"called":[109],"Diverse":[110],"Discriminative":[112],"representation":[113],"Learning":[114],"(DDLearn)":[115],"HAR.":[119],"DDLearn":[120,133,170],"simultaneously":[121],"considers":[122],"diversity":[123,137,148,154],"discrimination":[125,174],"learning.":[126,182],"With":[127],"constructed":[129],"self-supervised":[130],"learning":[131,176],"task,":[132],"enlarges":[134],"explores":[139],"latent":[141],"properties.":[143],"Then,":[144],"preservation":[149],"module":[150],"preserve":[152],"learned":[156],"features":[157],"by":[158,175,198],"enlarging":[159],"divergence":[162],"between":[163],"original":[165],"augmented":[167],"domains.":[168],"also":[171],"enhances":[172],"semantic":[173],"discriminative":[177],"representations":[178],"with":[179],"supervised":[180],"contrastive":[181],"Extensive":[183],"experiments":[184],"three":[186],"public":[187],"datasets":[189],"demonstrate":[190],"our":[192],"method":[193],"significantly":[194],"outperforms":[195],"state-of-art":[196],"methods":[197],"an":[199],"average":[200],"accuracy":[201],"improvement":[202],"9.5%":[204],"under":[205],"scenarios,":[210],"while":[211],"being":[212],"generic,":[214],"explainable,":[215],"flexible":[217],"framework.":[218],"Code":[219],"available":[221],"at:":[222],"https://github.com/microsoft/robustlearn.":[223]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":5}],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-10-10T00:00:00"}