{"id":"https://openalex.org/W7135201749","doi":"https://doi.org/10.48550/arxiv.2603.11917","title":"PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation","display_name":"PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation","publication_year":2026,"publication_date":"2026-03-12","ids":{"openalex":"https://openalex.org/W7135201749","doi":"https://doi.org/10.48550/arxiv.2603.11917"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.11917","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.11917","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.2603.11917","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5092506176","display_name":"Pietro Bonazzi","orcid":"https://orcid.org/0009-0006-6147-2214"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Bonazzi, Pietro","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033981189","display_name":"Nicola Farronato","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Farronato, Nicola","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106689444","display_name":"Stefan Zihlmann","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zihlmann, Stefan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129094244","display_name":"Haotong Qin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qin, Haotong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128951774","display_name":"Michele Magno","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Magno, Michele","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5092506176"],"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/T10036","display_name":"Advanced Neural Network Applications","score":0.4973999857902527,"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":0.4973999857902527,"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/T10273","display_name":"IoT and Edge/Fog Computing","score":0.06310000270605087,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T13553","display_name":"Age of Information Optimization","score":0.05950000137090683,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.7002999782562256},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5454000234603882},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5299999713897705},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4228000044822693},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41659998893737793},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.38989999890327454},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.3790000081062317},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.35249999165534973},{"id":"https://openalex.org/keywords/machine-vision","display_name":"Machine vision","score":0.3361000120639801}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7103999853134155},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7002999782562256},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6894999742507935},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5454000234603882},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5299999713897705},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.47369998693466187},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4228000044822693},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41659998893737793},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.38989999890327454},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.3790000081062317},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.35249999165534973},{"id":"https://openalex.org/C5339829","wikidata":"https://www.wikidata.org/wiki/Q1425977","display_name":"Machine vision","level":2,"score":0.3361000120639801},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.3343000113964081},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.33239999413490295},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.31060001254081726},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.30379998683929443},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.29980000853538513},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.29910001158714294},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.2980000078678131},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.29760000109672546},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.28850001096725464},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2851000130176544},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.28369998931884766},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.27790001034736633},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2702000141143799},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.2648000121116638}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.11917","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.11917","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.2603.11917","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.11917","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Real-time,":[0],"on-device":[1],"segmentation":[2,24,143],"is":[3,144],"critical":[4],"for":[5,27],"latency-sensitive":[6],"and":[7,14,29,45,60,65,69,74,81,115,136],"privacy-aware":[8],"applications":[9],"such":[10],"as":[11],"smart":[12],"glasses":[13],"Internet-of-Things":[15],"devices.":[16],"We":[17],"introduce":[18],"PicoSAM3,":[19],"a":[20,47],"lightweight":[21],"promptable":[22,142],"visual":[23],"model":[25,92],"optimized":[26],"edge":[28],"in-sensor":[30,101],"execution,":[31],"including":[32],"deployment":[33],"on":[34,107],"the":[35,108,148],"Sony":[36],"IMX500":[37],"vision":[38],"sensor.":[39],"PicoSAM3":[40,71],"has":[41],"1.3":[42],"M":[43],"parameters":[44],"combines":[46],"dense":[48],"CNN":[49],"architecture":[50],"with":[51,95,112],"region":[52],"of":[53],"interest":[54],"prompt":[55],"encoding,":[56],"Efficient":[57],"Channel":[58],"Attention,":[59],"knowledge":[61],"distillation":[62,122],"from":[63,123],"SAM2":[64],"SAM3.":[66],"On":[67],"COCO":[68],"LVIS,":[70],"achieves":[72],"65.45%":[73],"64.01%":[75],"mIoU,":[76],"respectively,":[77],"outperforming":[78],"existing":[79],"SAM-based":[80],"edge-oriented":[82],"baselines":[83],"at":[84,103,147],"similar":[85],"or":[86],"lower":[87],"complexity.":[88],"The":[89],"INT8":[90],"quantized":[91],"preserves":[93],"accuracy":[94],"negligible":[96],"degradation":[97],"while":[98],"enabling":[99],"real-time":[100],"inference":[102],"11.82":[104],"ms":[105],"latency":[106],"IMX500,":[109],"fully":[110],"complying":[111],"its":[113],"memory":[114],"operator":[116],"constraints.":[117],"Ablation":[118],"studies":[119],"show":[120],"that":[121,138],"large":[124],"SAM":[125],"models":[126],"yields":[127],"up":[128],"to":[129],"+14.5%":[130],"mIoU":[131],"improvement":[132],"over":[133],"supervised":[134],"training":[135],"demonstrate":[137],"high-quality,":[139],"spatially":[140],"flexible":[141],"feasible":[145],"directly":[146],"sensor":[149],"level.":[150]},"counts_by_year":[],"updated_date":"2026-03-14T06:46:50.379900","created_date":"2026-03-14T00:00:00"}