{"id":"https://openalex.org/W7166652509","doi":"https://doi.org/10.7148/2026-0379","title":"Combining open-vocabulary detection with foundation models for object recognition in an industrial bin picking context","display_name":"Combining open-vocabulary detection with foundation models for object recognition in an industrial bin picking context","publication_year":2026,"publication_date":"2026-06-23","ids":{"openalex":"https://openalex.org/W7166652509","doi":"https://doi.org/10.7148/2026-0379"},"language":null,"primary_location":{"id":"doi:10.7148/2026-0379","is_oa":true,"landing_page_url":"https://doi.org/10.7148/2026-0379","pdf_url":"https://doi.org/10.7148/2026-0379","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ECMS 2026 Proceedings edited by Filippo Sanfilippo, Florenc Demrozi, Fabio Sgarbossa, Mohammad Poursina","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.7148/2026-0379","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5011168487","display_name":"El Houssein Chouaib Harik","orcid":"https://orcid.org/0000-0002-6536-6535"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"El Houssein Chouaib Harik","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093204140","display_name":"Pirmin Sigron","orcid":"https://orcid.org/0000-0002-7864-7301"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pirmin Sigron","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139671901","display_name":"Stein Oskar Hinderaker","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stein Oskar Hinderaker","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5103186108","display_name":"Mohamed Riad Yagoubi","orcid":"https://orcid.org/0000-0002-4657-0637"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mohamed Riad Yagoubi","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":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"379","last_page":"388"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.4771000146865845,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.4771000146865845,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.1378999948501587,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.05260000005364418,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6941999793052673},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5543000102043152},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48080000281333923},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.4499000012874603},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.44609999656677246},{"id":"https://openalex.org/keywords/cad","display_name":"CAD","score":0.44510000944137573},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.43389999866485596},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.41920000314712524},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.37860000133514404}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7361000180244446},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6941999793052673},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6747000217437744},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5543000102043152},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48080000281333923},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.4499000012874603},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.44609999656677246},{"id":"https://openalex.org/C194789388","wikidata":"https://www.wikidata.org/wiki/Q17855283","display_name":"CAD","level":2,"score":0.44510000944137573},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.438400000333786},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.43389999866485596},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.41920000314712524},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.37860000133514404},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.3763999938964844},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.35199999809265137},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3424000144004822},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.3328999876976013},{"id":"https://openalex.org/C156273044","wikidata":"https://www.wikidata.org/wiki/Q4913766","display_name":"Bin","level":2,"score":0.3068999946117401},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2955000102519989},{"id":"https://openalex.org/C2778712577","wikidata":"https://www.wikidata.org/wiki/Q3505966","display_name":"Retraining","level":2,"score":0.2840999960899353},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.2793000042438507},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.2777000069618225},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2759000062942505},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.27379998564720154},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.26579999923706055},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.2606000006198883},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.25270000100135803},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.7148/2026-0379","is_oa":true,"landing_page_url":"https://doi.org/10.7148/2026-0379","pdf_url":"https://doi.org/10.7148/2026-0379","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ECMS 2026 Proceedings edited by Filippo Sanfilippo, Florenc Demrozi, Fabio Sgarbossa, Mohammad Poursina","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.7148/2026-0379","is_oa":true,"landing_page_url":"https://doi.org/10.7148/2026-0379","pdf_url":"https://doi.org/10.7148/2026-0379","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ECMS 2026 Proceedings edited by Filippo Sanfilippo, Florenc Demrozi, Fabio Sgarbossa, Mohammad Poursina","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.5236865282058716,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7166652509.pdf","grobid_xml":"https://content.openalex.org/works/W7166652509.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Object":[0],"recognition":[1],"for":[2,46,50],"industrial":[3,115],"bin":[4],"picking":[5],"traditionally":[6],"requires":[7,67],"large":[8],"annotated":[9],"datasets":[10],"and":[11,53,74,120,153],"retraining":[12],"when":[13],"new":[14],"parts":[15],"are":[16],"introduced.":[17],"The":[18],"sim-to-real":[19,89],"gap":[20,90],"further":[21,134],"limits":[22],"approaches":[23],"that":[24,43,136,154],"rely":[25,76],"on":[26,61,69,78,97,105,125],"synthetic":[27,70,130,143],"training":[28,68,131,160],"data":[29],"alone.":[30],"In":[31],"this":[32],"paper,":[33],"we":[34],"present":[35],"an":[36],"initial":[37],"investigation":[38],"into":[39],"a":[40,54,92],"three-stage":[41],"pipeline":[42],"combines":[44],"GroundingDINO":[45],"open-vocabulary":[47],"detection,":[48],"SAM2":[49],"instance":[51],"segmentation,":[52],"lightweight":[55,94],"Multi-Layer":[56],"Perceptron":[57],"(MLP)":[58],"classifier":[59,66,162],"operating":[60,96],"DINOv2":[62,137],"features.":[63,100],"Only":[64],"the":[65,79,88,106],"CAD":[71],"renders;":[72],"detection":[73],"segmentation":[75],"entirely":[77],"zero-shot":[80],"capabilities":[81],"of":[82],"foundation":[83],"models.":[84],"This":[85],"design":[86],"isolates":[87],"to":[91,144],"single":[93],"component":[95],"robust,":[98],"pre-trained":[99],"We":[101,133],"evaluate":[102],"our":[103],"approach":[104],"challenging":[107],"T-LESS":[108],"dataset,":[109],"which":[110],"features":[111,138],"textureless,":[112],"visually":[113],"similar":[114],"objects":[116,158],"with":[117],"frequent":[118],"symmetries,":[119],"achieve":[121],"70.0%":[122],"class-level":[123],"F1":[124],"real":[126,145],"images":[127],"using":[128],"only":[129],"data.":[132],"show":[135],"transfer":[139],"more":[140],"robustly":[141],"from":[142],"domains":[146],"than":[147],"alternative":[148],"backbones":[149],"such":[150],"as":[151],"SigLIP,":[152],"incorporating":[155],"realistic":[156],"distractor":[157],"during":[159],"improves":[161],"discrimination.":[163]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-07-01T00:00:00"}