{"id":"https://openalex.org/W7133315761","doi":"https://doi.org/10.48550/arxiv.2603.01029","title":"Vision-Language Feature Alignment for Road Anomaly Segmentation","display_name":"Vision-Language Feature Alignment for Road Anomaly Segmentation","publication_year":2026,"publication_date":"2026-03-01","ids":{"openalex":"https://openalex.org/W7133315761","doi":"https://doi.org/10.48550/arxiv.2603.01029"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.01029","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01029","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.01029","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111240428","display_name":"Zhuolin He","orcid":"https://orcid.org/0000-0003-4817-6227"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"He, Zhuolin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127886804","display_name":"Jiacheng Tang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tang, Jiacheng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128008058","display_name":"Jian Pu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pu, Jian","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128021354","display_name":"Xiangyang Xue","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xue, Xiangyang","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5111240428"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.31439998745918274,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.31439998745918274,"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"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.26759999990463257,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.21960000693798065,"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/anomaly-detection","display_name":"Anomaly detection","score":0.6887000203132629},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6815000176429749},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6297000050544739},{"id":"https://openalex.org/keywords/spurious-relationship","display_name":"Spurious relationship","score":0.5929999947547913},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.546500027179718},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5400999784469604},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.48030000925064087},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.46810001134872437}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7044000029563904},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6887000203132629},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6815000176429749},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6416000127792358},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6297000050544739},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.5929999947547913},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.546500027179718},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5400999784469604},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.48030000925064087},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.46810001134872437},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44929999113082886},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.41110000014305115},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.4023999869823456},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.376800000667572},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34380000829696655},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.34310001134872437},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.3400000035762787},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32850000262260437},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3237000107765198},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.29989999532699585},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2768999934196472},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.2671000063419342},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2619999945163727},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.25529998540878296}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.01029","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01029","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.01029","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01029","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Safe":[0],"autonomous":[1],"systems":[2],"in":[3,110],"complex":[4],"environments":[5],"require":[6],"robust":[7],"road":[8],"anomaly":[9,73,108,136],"segmentation":[10,74],"to":[11,23,33,98],"identify":[12],"unknown":[13],"obstacles.":[14],"However,":[15],"existing":[16],"approaches":[17],"often":[18],"rely":[19],"on":[20,37,151,161],"pixel-level":[21],"statistics":[22],"determine":[24],"whether":[25],"a":[26,71,88,119],"region":[27],"appears":[28],"anomalous.":[29],"This":[30],"reliance":[31],"leads":[32],"high":[34],"false-positive":[35],"rates":[36],"semantically":[38],"normal":[39],"background":[40,111],"regions":[41],"such":[42],"as":[43],"sky":[44],"or":[45],"vegetation,":[46],"and":[47,62,130,157],"poor":[48],"recall":[49],"of":[50,102],"true":[51],"Out-of-distribution":[52],"(OOD)":[53],"instances,":[54],"thereby":[55],"posing":[56],"safety":[57],"risks":[58],"for":[59],"robotic":[60],"perception":[61],"decision-making.":[63],"To":[64],"address":[65],"these":[66],"challenges,":[67],"we":[68,86,116],"propose":[69],"VL-Anomaly,":[70],"vision-language":[72],"framework":[75],"that":[76,93,123,146],"incorporates":[77],"semantic":[78],"priors":[79],"from":[80],"pre-trained":[81],"Vision-Language":[82],"Models":[83],"(VLMs).":[84],"Specifically,":[85],"design":[87],"prompt":[89],"learning-driven":[90],"alignment":[91],"module":[92],"adapts":[94],"Mask2Forme's":[95],"visual":[96],"features":[97],"CLIP":[99],"text":[100],"embeddings":[101],"known":[103],"categories,":[104],"effectively":[105],"suppressing":[106],"spurious":[107],"responses":[109],"regions.":[112],"At":[113],"inference":[114,121],"time,":[115],"further":[117],"introduce":[118],"multi-source":[120],"strategy":[122],"integrates":[124],"text-guided":[125],"similarity,":[126],"CLIP-based":[127],"image-text":[128],"similarity":[129],"detector":[131],"confidence,":[132],"enabling":[133],"more":[134],"reliable":[135],"prediction":[137],"by":[138],"leveraging":[139],"complementary":[140],"information":[141],"sources.":[142],"Extensive":[143],"experiments":[144],"demonstrate":[145],"VL-Anomaly":[147],"achieves":[148],"state-of-the-art":[149],"performance":[150],"benchmark":[152],"datasets":[153],"including":[154],"RoadAnomaly,":[155],"SMIYC":[156],"Fishyscapes.Code":[158],"is":[159],"released":[160],"https://github.com/NickHezhuolin/VL-aligner-Road-anomaly-segment.":[162]},"counts_by_year":[],"updated_date":"2026-03-04T07:09:34.246503","created_date":"2026-03-04T00:00:00"}
