{"id":"https://openalex.org/W7161298282","doi":"https://doi.org/10.48550/arxiv.2605.14795","title":"COAL: Counterfactual and Observation-Enhanced Alignment Learning for Discriminative Referring Multi-Object Tracking","display_name":"COAL: Counterfactual and Observation-Enhanced Alignment Learning for Discriminative Referring Multi-Object Tracking","publication_year":2026,"publication_date":"2026-05-14","ids":{"openalex":"https://openalex.org/W7161298282","doi":"https://doi.org/10.48550/arxiv.2605.14795"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.14795","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14795","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.14795","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5024057421","display_name":"Shukun Jia","orcid":"https://orcid.org/0000-0002-7379-2534"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jia, Shukun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136211570","display_name":"Shiyu Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Shiyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136222306","display_name":"Yipei Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yipei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122308768","display_name":"Ximeng Cheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Ximeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136192431","display_name":"Yichao Cao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Yichao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136204696","display_name":"Xiaobo Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Xiaobo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.754800021648407,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.754800021648407,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.04039999842643738,"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":0.028999999165534973,"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":0.772599995136261},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.6480000019073486},{"id":"https://openalex.org/keywords/counterfactual-thinking","display_name":"Counterfactual thinking","score":0.5990999937057495},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.5401999950408936},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.41929998993873596},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.41130000352859497},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.39559999108314514}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.772599995136261},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.739799976348877},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6761999726295471},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.6480000019073486},{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.5990999937057495},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5870000123977661},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.5401999950408936},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.41929998993873596},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.41130000352859497},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.39559999108314514},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.3711000084877014},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.33970001339912415},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.29170000553131104},{"id":"https://openalex.org/C2776224158","wikidata":"https://www.wikidata.org/wiki/Q187931","display_name":"Phrase","level":2,"score":0.28349998593330383},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.27900001406669617},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.27709999680519104},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.263700008392334},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2614000141620636}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.14795","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14795","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.14795","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14795","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7533490061759949,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Referring":[0],"Multi-Object":[1],"Tracking":[2],"(RMOT)":[3],"faces":[4],"a":[5,64,84,120],"fundamental":[6],"structural":[7,71],"contradiction":[8],"between":[9],"the":[10,14,88,146,152,159,166],"high-discriminability":[11],"demand":[12],"and":[13,50,60,91,137],"sparse":[15,37],"semantic":[16,51],"supervision.":[17],"This":[18],"mismatch":[19],"is":[20],"particularly":[21],"acute":[22],"in":[23,169],"highly":[24,153],"homogeneous":[25],"scenarios":[26],"that":[27,66],"require":[28],"fine-grained":[29],"discrimination":[30],"over":[31],"complex":[32],"compositional":[33,113],"semantics.":[34],"However,":[35],"under":[36],"supervision,":[38,106],"models":[39],"overfit":[40],"to":[41,86,104],"salient":[42],"yet":[43],"insufficient":[44],"cues,":[45],"thereby":[46],"encouraging":[47],"shortcut":[48],"learning":[49],"collapse.":[52],"To":[53],"resolve":[54],"this,":[55],"we":[56,77,99],"propose":[57,100],"COAL":[58],"(Counterfactual":[59],"Observation-enhanced":[61],"Alignment":[62],"Learning),":[63],"framework":[65],"advances":[67],"RMOT":[68],"beyond":[69],"isolated":[70],"optimization":[72],"through":[73],"knowledge":[74,129,162],"regularization.":[75],"First,":[76],"introduce":[78],"Explicit":[79],"Semantic":[80],"Injection":[81],"(ESI)":[82],"via":[83],"VLM":[85],"densify":[87],"observation":[89],"space":[90],"enhance":[92],"instance":[93],"discriminability.":[94],"Second,":[95],"leveraging":[96],"LLM":[97],"reasoning,":[98],"Counterfactual":[101],"Learning":[102],"(CFL)":[103],"augment":[105],"enforcing":[107],"strict":[108],"attribute":[109],"verification":[110],"for":[111,164],"robust":[112],"recognition.":[114],"These":[115,156],"strategies":[116],"are":[117],"unified":[118],"within":[119],"Hierarchical":[121],"Multi-Stream":[122],"Integration":[123],"(HMSI)":[124],"architecture,":[125],"which":[126],"distills":[127],"external":[128],"into":[130],"domain-specific":[131],"discriminative":[132],"representations.":[133],"Experiments":[134],"on":[135,151],"Refer-KITTI":[136],"Refer-KITTI-V2":[138],"benchmarks":[139],"validate":[140],"COAL's":[141],"efficacy.":[142],"Notably,":[143],"it":[144],"surpasses":[145],"state-of-the-art":[147],"by":[148],"7.28%":[149],"HOTA":[150],"challenging":[154],"Refer-KITTI-V2.":[155],"results":[157],"demonstrate":[158],"effectiveness":[160],"of":[161],"regularization":[163],"resolving":[165],"sparsity-discriminability":[167],"paradox":[168],"RMOT.":[170]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-16T00:00:00"}
