{"id":"https://openalex.org/W7130392578","doi":"https://doi.org/10.48550/arxiv.2602.15154","title":"Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories","display_name":"Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories","publication_year":2026,"publication_date":"2026-02-16","ids":{"openalex":"https://openalex.org/W7130392578","doi":"https://doi.org/10.48550/arxiv.2602.15154"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2602.15154","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.15154","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.2602.15154","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5004973925","display_name":"Praditha Alwis","orcid":"https://orcid.org/0000-0002-4052-2757"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Alwis, Praditha","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108892773","display_name":"Soumyadeep Chandra","orcid":"https://orcid.org/0009-0002-0182-3618"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chandra, Soumyadeep","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126349354","display_name":"Deepak Ravikumar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ravikumar, Deepak","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5126282656","display_name":"Kaushik Roy","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Roy, Kaushik","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5004973925"],"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/T10812","display_name":"Human Pose and Action Recognition","score":0.5543000102043152,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.5543000102043152,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.1728000044822693,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.08399999886751175,"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/annotation","display_name":"Annotation","score":0.7833999991416931},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.6283000111579895},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.5715000033378601},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5672000050544739},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5458999872207642},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5206000208854675},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.4717000126838684},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4244999885559082}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8003000020980835},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.7833999991416931},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.6283000111579895},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6194999814033508},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.5715000033378601},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5672000050544739},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5458999872207642},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5206000208854675},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.4717000126838684},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4244999885559082},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36809998750686646},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3538999855518341},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.35040000081062317},{"id":"https://openalex.org/C193519340","wikidata":"https://www.wikidata.org/wiki/Q891179","display_name":"Data loss","level":2,"score":0.32190001010894775},{"id":"https://openalex.org/C103088060","wikidata":"https://www.wikidata.org/wiki/Q1062839","display_name":"Error detection and correction","level":2,"score":0.32109999656677246},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.31679999828338623},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3073999881744385},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.302700012922287},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.3010999858379364},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.2912999987602234},{"id":"https://openalex.org/C172849965","wikidata":"https://www.wikidata.org/wiki/Q3148875","display_name":"Reference frame","level":3,"score":0.290800005197525},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28529998660087585},{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.2736000120639801},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.26829999685287476},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.2623000144958496}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2602.15154","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.15154","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.2602.15154","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.15154","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":[{"id":"https://metadata.un.org/sdg/4","score":0.45852959156036377,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"High-quality":[0],"video":[1,22,148],"datasets":[2,23],"are":[3,33,52,160,179],"foundational":[4],"for":[5,69,125,184,233],"training":[6,94,238],"robust":[7],"models":[8],"in":[9,55,170,240],"tasks":[10],"like":[11],"action":[12],"recognition,":[13],"phase":[14],"detection,":[15],"and":[16,38,151,202,210,223,236],"event":[17],"segmentation.":[18],"However,":[19],"many":[20],"real-world":[21],"suffer":[24],"from":[25],"annotation":[26,71,185,200],"errors":[27,51,72,201],"such":[28,220],"as":[29,80,101,121,181,221],"*mislabeling*,":[30],"where":[31,40,58],"segments":[32],"assigned":[34],"incorrect":[35],"class":[36],"labels,":[37],"*disordering*,":[39],"the":[41,47,75,81,126,165],"temporal":[42,59,190],"sequence":[43],"does":[44,194],"not":[45,195],"follow":[46],"correct":[48],"progression.":[49],"These":[50,158],"particularly":[53],"harmful":[54],"phase-annotated":[56],"tasks,":[57],"consistency":[60],"is":[61,203],"critical.":[62],"We":[63],"propose":[64],"a":[65,84,102,147,171,230],"novel,":[66],"model-agnostic":[67],"method":[68,193],"detecting":[70],"by":[73],"analyzing":[74],"Cumulative":[76],"Sample":[77],"Loss":[78],"(CSL)--defined":[79],"average":[82],"loss":[83,98,119,140,166],"frame":[85,169,224],"incurs":[86],"when":[87],"passing":[88],"through":[89],"model":[90,127,150],"checkpoints":[91,159],"saved":[92],"across":[93,205],"epochs.":[95],"This":[96],"per-frame":[97],"trajectory":[99],"acts":[100],"dynamic":[103],"fingerprint":[104],"of":[105,167],"frame-level":[106],"learnability.":[107],"Mislabeled":[108],"or":[109,117,189],"disordered":[110],"frames":[111,135],"tend":[112],"to":[113,128,138,163],"show":[114],"consistently":[115],"high":[116,177],"irregular":[118],"patterns,":[120],"they":[122],"remain":[123],"difficult":[124],"learn":[129],"throughout":[130],"training,":[131],"while":[132],"correctly":[133],"labeled":[134],"typically":[136],"converge":[137],"low":[139],"early.":[141],"To":[142],"compute":[143],"CSL,":[144],"we":[145],"train":[146],"segmentation":[149],"store":[152],"its":[153],"weights":[154],"at":[155],"each":[156,168],"epoch.":[157],"then":[161],"used":[162],"evaluate":[164],"test":[172],"video.":[173],"Frames":[174],"with":[175],"persistently":[176],"CSL":[178],"flagged":[180],"likely":[182],"candidates":[183],"errors,":[186],"including":[187],"mislabeling":[188,222],"misalignment.":[191],"Our":[192],"require":[196],"ground":[197],"truth":[198],"on":[199,208],"generalizable":[204],"datasets.":[206],"Experiments":[207],"EgoPER":[209],"Cholec80":[211],"demonstrate":[212],"strong":[213],"detection":[214],"performance,":[215],"effectively":[216],"identifying":[217],"subtle":[218],"inconsistencies":[219],"disordering.":[225],"The":[226],"proposed":[227],"approach":[228],"provides":[229],"powerful":[231],"tool":[232],"dataset":[234],"auditing":[235],"improving":[237],"reliability":[239],"video-based":[241],"machine":[242],"learning.":[243]},"counts_by_year":[],"updated_date":"2026-02-19T06:31:58.851227","created_date":"2026-02-19T00:00:00"}