{"id":"https://openalex.org/W4417182977","doi":"https://doi.org/10.1109/iccv51701.2025.00050","title":"Gradient Short-Circuit: Efficient Out-of-Distribution Detection via Feature Intervention","display_name":"Gradient Short-Circuit: Efficient Out-of-Distribution Detection via Feature Intervention","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W4417182977","doi":"https://doi.org/10.1109/iccv51701.2025.00050"},"language":"en","primary_location":{"id":"doi:10.1109/iccv51701.2025.00050","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv51701.2025.00050","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 (ICCV)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2507.01417","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101908761","display_name":"Jiawei Gu","orcid":"https://orcid.org/0000-0003-2650-6870"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiawei Gu","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009278730","display_name":"Ziyue Qiao","orcid":null},"institutions":[{"id":"https://openalex.org/I2799285752","display_name":"Bay Institute","ror":"https://ror.org/02bgarq81","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I2799285752"]},{"id":"https://openalex.org/I4396570619","display_name":"Great Bay University","ror":"https://ror.org/01hdgge16","country_code":null,"type":"education","lineage":["https://openalex.org/I4396570619"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ziyue Qiao","raw_affiliation_strings":["School of Computing and Information Technology, Great Bay University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing and Information Technology, Great Bay University","institution_ids":["https://openalex.org/I2799285752","https://openalex.org/I4396570619"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017096005","display_name":"Zechao Li","orcid":"https://orcid.org/0000-0002-5341-5985"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zechao Li","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology","institution_ids":["https://openalex.org/I36399199"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.36555278,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"457","last_page":"466"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.6126999855041504,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.6126999855041504,"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":0.14740000665187836,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.09780000150203705,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/spurious-relationship","display_name":"Spurious relationship","score":0.7659000158309937},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6273999810218811},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6100999712944031},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5515999794006348},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.43380001187324524},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.4259999990463257},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.4050999879837036}],"concepts":[{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.7659000158309937},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.667900025844574},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6541000008583069},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6273999810218811},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6100999712944031},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5515999794006348},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.43380001187324524},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.4259999990463257},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.4050999879837036},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.39070001244544983},{"id":"https://openalex.org/C115680565","wikidata":"https://www.wikidata.org/wiki/Q5977448","display_name":"Gradient method","level":2,"score":0.3440999984741211},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3280999958515167},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.3160000145435333},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.31279999017715454},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.3091000020503998},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2768000066280365},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2542000114917755},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/iccv51701.2025.00050","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv51701.2025.00050","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 (ICCV)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2507.01417","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.01417","pdf_url":"https://arxiv.org/pdf/2507.01417","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2507.01417","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2507.01417","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2507.01417","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.01417","pdf_url":"https://arxiv.org/pdf/2507.01417","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1321842044","display_name":null,"funder_award_id":"62425603,62406056","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"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Out-of-Distribution":[0],"(OOD)":[1],"detection":[2,159],"is":[3,141],"critical":[4],"for":[5,46],"safely":[6],"deploying":[7],"deep":[8],"models":[9],"in":[10,58,65,160],"open-world":[11],"environments,":[12],"where":[13],"inputs":[14],"may":[15],"lie":[16],"outside":[17],"the":[18,42,66,99,103,119,139,148],"training":[19],"distribution.":[20],"During":[21],"inference":[22,150],"on":[23,128],"a":[24,34,112,123,153],"model":[25],"trained":[26],"exclusively":[27],"with":[28],"In-Distribution":[29],"(ID)":[30],"data,":[31],"we":[32,73,109],"observe":[33],"salient":[35],"gradient":[36,44,63,107],"phenomenon:":[37],"around":[38],"an":[39,75],"ID":[40,93],"sample,":[41],"local":[43,113],"directions":[45,64],"\"enhancing\"":[47],"that":[48,83,116],"sample's":[49],"predicted":[50],"class":[51],"remain":[52],"relatively":[53],"consistent,":[54],"whereas":[55],"OOD":[56,89,130,158],"samples--unseen":[57],"training--exhibit":[59],"disorganized":[60],"or":[61],"conflicting":[62],"same":[67],"neighborhood.":[68],"Motivated":[69],"by":[70],"this":[71,106],"observation,":[72],"propose":[74],"inference-stage":[76],"technique":[77],"to":[78,87,147],"short-circuit":[79],"those":[80],"feature":[81],"coordinates":[82],"spurious":[84],"gradients":[85],"exploit":[86],"inflate":[88],"confidence,":[90],"while":[91],"leaving":[92],"classification":[94],"largely":[95],"intact.":[96],"To":[97],"circumvent":[98],"expense":[100],"of":[101],"recomputing":[102],"logits":[104],"after":[105],"short-circuit,":[108],"further":[110],"introduce":[111],"first-order":[114],"approximation":[115],"accurately":[117],"captures":[118],"post-modification":[120],"outputs":[121],"without":[122],"second":[124],"forward":[125],"pass.":[126],"Experiments":[127],"standard":[129,149],"benchmarks":[131],"show":[132],"our":[133],"approach":[134],"yields":[135],"substantial":[136],"improvements.":[137],"Moreover,":[138],"method":[140],"lightweight":[142],"and":[143],"requires":[144],"minimal":[145],"changes":[146],"pipeline,":[151],"offering":[152],"practical":[154],"path":[155],"toward":[156],"robust":[157],"real-world":[161],"applications.":[162]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}