{"id":"https://openalex.org/W7131093782","doi":"https://doi.org/10.1109/iccvw69036.2025.00191","title":"Robust Scenario Mining Assisted by Multimodal Semantics","display_name":"Robust Scenario Mining Assisted by Multimodal Semantics","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W7131093782","doi":"https://doi.org/10.1109/iccvw69036.2025.00191"},"language":null,"primary_location":{"id":"doi:10.1109/iccvw69036.2025.00191","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvw69036.2025.00191","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)","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/A5100606731","display_name":"Yifei Chen","orcid":"https://orcid.org/0009-0001-9475-0480"},"institutions":[{"id":"https://openalex.org/I4210131919","display_name":"Xi'an University of Technology","ror":"https://ror.org/038avdt50","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210131919"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yifei Chen","raw_affiliation_strings":["Xi&#x0027;an University of Technology"],"affiliations":[{"raw_affiliation_string":"Xi&#x0027;an University of Technology","institution_ids":["https://openalex.org/I4210131919"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040697381","display_name":"Ross Greer","orcid":"https://orcid.org/0000-0001-8595-0379"},"institutions":[{"id":"https://openalex.org/I156087764","display_name":"University of California, Merced","ror":"https://ror.org/00d9ah105","country_code":"US","type":"education","lineage":["https://openalex.org/I156087764"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ross Greer","raw_affiliation_strings":["University of California,Merced"],"affiliations":[{"raw_affiliation_string":"University of California,Merced","institution_ids":["https://openalex.org/I156087764"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100606731"],"corresponding_institution_ids":["https://openalex.org/I4210131919"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.88177437,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1813","last_page":"1822"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.3052000105381012,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.3052000105381012,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.08020000159740448,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T11596","display_name":"Constraint Satisfaction and Optimization","score":0.06270000338554382,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/executable","display_name":"Executable","score":0.7577000260353088},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5649999976158142},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.47130000591278076},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.45829999446868896},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.43630000948905945},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.4032000005245209},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.3903999924659729},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.385699987411499},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.3725000023841858}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8025000095367432},{"id":"https://openalex.org/C160145156","wikidata":"https://www.wikidata.org/wiki/Q778586","display_name":"Executable","level":2,"score":0.7577000260353088},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5649999976158142},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4733999967575073},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.47130000591278076},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.45829999446868896},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45730000734329224},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.43630000948905945},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.4032000005245209},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.3903999924659729},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.385699987411499},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3725000023841858},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.3702999949455261},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.36649999022483826},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.365200012922287},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3407999873161316},{"id":"https://openalex.org/C2779982483","wikidata":"https://www.wikidata.org/wiki/Q6094420","display_name":"Iterative refinement","level":2,"score":0.32839998602867126},{"id":"https://openalex.org/C133162039","wikidata":"https://www.wikidata.org/wiki/Q1061077","display_name":"Code generation","level":3,"score":0.3084000051021576},{"id":"https://openalex.org/C31266012","wikidata":"https://www.wikidata.org/wiki/Q6554340","display_name":"Linkage (software)","level":3,"score":0.2953000068664551},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2937000095844269},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.289000004529953},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.2752000093460083},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.2727999985218048},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.25999999046325684},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.25220000743865967},{"id":"https://openalex.org/C2775955345","wikidata":"https://www.wikidata.org/wiki/Q7449071","display_name":"Semantic mapping","level":2,"score":0.251800000667572}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccvw69036.2025.00191","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvw69036.2025.00191","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)","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":23,"referenced_works":["https://openalex.org/W2991022776","https://openalex.org/W3031993934","https://openalex.org/W3035172746","https://openalex.org/W3035574168","https://openalex.org/W3043840704","https://openalex.org/W3086436251","https://openalex.org/W3120338137","https://openalex.org/W3174873881","https://openalex.org/W4312372711","https://openalex.org/W4382059335","https://openalex.org/W4385804768","https://openalex.org/W4386072365","https://openalex.org/W4402671783","https://openalex.org/W4402704596","https://openalex.org/W4402727068","https://openalex.org/W4402754221","https://openalex.org/W4409263329","https://openalex.org/W4409917592","https://openalex.org/W4409917782","https://openalex.org/W4410638172","https://openalex.org/W4415068890","https://openalex.org/W4416183328","https://openalex.org/W6949549905"],"related_works":[],"abstract_inverted_index":{"Scenario":[0],"mining":[1,131],"from":[2,81],"large-scale":[3],"autonomous":[4,19],"driving":[5,20],"datasets,":[6],"such":[7],"as":[8],"Argoverse":[9,177],"2,":[10],"is":[11,52,147],"crucial":[12],"for":[13,42,89,110,201],"the":[14,47,58,66,125,154,166,195,198],"development":[15],"and":[16,75,84,169],"validation":[17],"of":[18,49,60,68,137,172,197],"systems.":[21],"The":[22],"RefAV":[23],"framework":[24],"represents":[25],"a":[26,69,103,107,117,141],"promising":[27],"approach":[28],"by":[29,54,120,129,152],"employing":[30],"Large":[31],"Language":[32],"Models":[33],"(LLMs)":[34],"to":[35,164],"translate":[36],"natural-language":[37,73,126],"queries":[38],"into":[39],"executable":[40],"code":[41,144,151],"identifying":[43],"relevant":[44],"scenarios.":[45],"However,":[46],"performance":[48],"this":[50],"method":[51,104,185],"constrained":[53],"its":[55],"reliance":[56],"on":[57,176],"quality":[59],"upstream":[61],"3D":[62],"multi-object":[63,94],"tracking":[64],"data,":[65],"absence":[67],"direct":[70],"linkage":[71],"between":[72],"descriptions":[74],"RGB":[76],"images,":[77],"runtime":[78],"errors":[79],"stemming":[80],"LLM-generated":[82,134],"code,":[83],"inaccuracies":[85],"in":[86],"interpreting":[87],"parameters":[88],"functions":[90],"that":[91,105,183],"describe":[92],"complex":[93],"spatial":[95],"relationships.":[96],"To":[97],"address":[98],"these":[99],"issues,":[100],"we":[101],"introduce":[102],"utilizes":[106],"CLIP":[108],"encoder":[109],"multimodal":[111],"semantic":[112],"similarity":[113],"filtering,":[114],"first":[115],"performing":[116],"coarse-grained":[118],"selection":[119],"comparing":[121],"raw":[122],"images":[123],"against":[124],"description,":[127],"followed":[128],"fine-grained":[130],"using":[132],"an":[133],"script":[135],"composed":[136],"atomic":[138],"functions.":[139,174],"Additionally,":[140],"fault-tolerant":[142],"iterative":[143],"generation":[145],"mechanism":[146],"introduced,":[148],"which":[149],"refines":[150],"reprompting":[153],"LLM":[155],"with":[156,160,179],"error":[157],"feedback,":[158],"along":[159],"specialized":[161],"prompt":[162],"engineering":[163],"enhance":[165],"LLM's":[167],"comprehension":[168],"correct":[170],"application":[171],"spatial-relationship":[173],"Experiments":[175],"2":[178],"various":[180],"LLMs":[181],"show":[182],"our":[184],"achieves":[186],"consistent":[187],"improvements":[188],"across":[189],"multiple":[190],"metrics.":[191],"These":[192],"results":[193],"underscore":[194],"efficacy":[196],"proposed":[199],"techniques":[200],"reliable,":[202],"high-precision":[203],"scenario":[204],"mining.":[205]},"counts_by_year":[],"updated_date":"2026-02-25T06:17:34.324206","created_date":"2026-02-24T00:00:00"}
