{"id":"https://openalex.org/W7165621600","doi":"https://doi.org/10.48550/arxiv.2606.21456","title":"Technical Report for ICRA 2026 GOOSE 2D Fine-Grained Semantic Segmentation Challenge: Exploring Query-Based Segmentation and Increased Spatial Context for Outdoor Scene Understanding","display_name":"Technical Report for ICRA 2026 GOOSE 2D Fine-Grained Semantic Segmentation Challenge: Exploring Query-Based Segmentation and Increased Spatial Context for Outdoor Scene Understanding","publication_year":2026,"publication_date":"2026-06-19","ids":{"openalex":"https://openalex.org/W7165621600","doi":"https://doi.org/10.48550/arxiv.2606.21456"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.21456","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.21456","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.21456","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5036264253","display_name":"David Pascual-Hern\u00e1ndez","orcid":"https://orcid.org/0000-0002-6549-1694"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pascual-Hern\u00e1ndez, David","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014331456","display_name":"Roberto Calvo-Palomino","orcid":"https://orcid.org/0000-0002-5417-6453"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Calvo-Palomino, Roberto","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091890497","display_name":"Inmaculada Mora-Jim\u00e9nez","orcid":"https://orcid.org/0000-0003-0735-367X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mora-Jim\u00e9nez, Inmaculada","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139160245","display_name":"Jose Mar\u00eda Ca\u00f1as-Plaza","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ca\u00f1as-Plaza, Jose Mar\u00eda","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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.39969998598098755,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.39969998598098755,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.36169999837875366,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.06530000269412994,"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/segmentation","display_name":"Segmentation","score":0.7472000122070312},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6093000173568726},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.5335000157356262},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.46860000491142273},{"id":"https://openalex.org/keywords/goose","display_name":"Goose","score":0.4350999891757965},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.4138999879360199},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4124000072479248}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7472000122070312},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6951000094413757},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6872000098228455},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6093000173568726},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.5335000157356262},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.46860000491142273},{"id":"https://openalex.org/C2781126499","wikidata":"https://www.wikidata.org/wiki/Q16529344","display_name":"Goose","level":2,"score":0.4350999891757965},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.4138999879360199},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4124000072479248},{"id":"https://openalex.org/C58642233","wikidata":"https://www.wikidata.org/wiki/Q8269924","display_name":"Taxonomy (biology)","level":2,"score":0.41110000014305115},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.388700008392334},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3797000050544739},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3319000005722046},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.31709998846054077},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2890999913215637},{"id":"https://openalex.org/C167651023","wikidata":"https://www.wikidata.org/wiki/Q1474611","display_name":"Plot (graphics)","level":2,"score":0.2890999913215637},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.2782999873161316},{"id":"https://openalex.org/C59519942","wikidata":"https://www.wikidata.org/wiki/Q650665","display_name":"Drone","level":2,"score":0.2773999869823456},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.265500009059906},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25540000200271606}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.21456","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.21456","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.21456","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.21456","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":"Preprint"},"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":{"In":[0],"this":[1],"report,":[2],"we":[3,68],"present":[4],"our":[5],"submission":[6],"to":[7,80],"the":[8,19,32,81,96,101,111,133],"GOOSE":[9,33],"2D":[10],"Fine-Grained":[11],"Semantic":[12],"Segmentation":[13],"Challenge,":[14],"organized":[15],"as":[16,65],"part":[17],"of":[18,54,113,130],"Workshop":[20],"on":[21,132],"Field":[22],"Robotics":[23],"at":[24,162],"ICRA":[25],"2026.":[26],"The":[27],"challenge":[28,134],"combines":[29],"data":[30],"from":[31,44,63],"and":[34,58,85,151,155],"GOOSE-Ex":[35],"datasets,":[36],"which":[37],"comprise":[38],"more":[39],"than":[40],"13k":[41],"images":[42],"captured":[43],"4":[45],"distinct":[46],"camera":[47],"setups,":[48],"annotated":[49],"using":[50,124],"a":[51,66,78,138],"hierarchical":[52],"taxonomy":[53],"56":[55],"fine-grained":[56,118,142],"classes":[57],"11":[59],"broader":[60],"categories.":[61],"Starting":[62],"SegFormer":[64],"baseline,":[67],"progressively":[69],"improve":[70],"segmentation":[71,93,144],"performance":[72],"through":[73],"increased":[74],"training":[75,106],"crop":[76,102],"sizes,":[77],"transition":[79],"query-based":[82,92],"Mask2Former":[83],"architecture,":[84],"test-time":[86,125],"augmentation.":[87],"Our":[88,121],"experiments":[89],"show":[90],"that":[91],"significantly":[94],"outperforms":[95],"baseline":[97,140],"model.":[98],"Furthermore,":[99],"increasing":[100],"size":[103],"used":[104],"during":[105],"yields":[107],"substantial":[108],"gains,":[109],"highlighting":[110],"relevance":[112],"preserving":[114],"scene":[115],"context":[116],"for":[117,141],"semantic":[119,143],"disambiguation.":[120],"final":[122],"submission,":[123],"augmentation,":[126],"achieves":[127],"an":[128],"mIoU":[129],"69.6%":[131],"test":[135],"set,":[136],"providing":[137],"strong":[139],"in":[145],"outdoor":[146],"environments.":[147],"To":[148],"facilitate":[149],"reproducibility":[150],"future":[152],"research,":[153],"code":[154],"weights":[156],"will":[157],"be":[158],"made":[159],"publicly":[160],"available":[161],"https://github.com/RoboticsLabURJC/outdoor-fine-grained-segmentation":[163],".":[164]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-24T00:00:00"}