{"id":"https://openalex.org/W4416748760","doi":"https://doi.org/10.1109/iros60139.2025.11247562","title":"Quaternion Approximate Networks for Enhanced Image Classification and Oriented Object Detection","display_name":"Quaternion Approximate Networks for Enhanced Image Classification and Oriented Object Detection","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W4416748760","doi":"https://doi.org/10.1109/iros60139.2025.11247562"},"language":null,"primary_location":{"id":"doi:10.1109/iros60139.2025.11247562","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros60139.2025.11247562","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5063784201","display_name":"Brian E. Grant","orcid":null},"institutions":[{"id":"https://openalex.org/I58956616","display_name":"Case Western Reserve University","ror":"https://ror.org/051fd9666","country_code":"US","type":"education","lineage":["https://openalex.org/I58956616"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Bryce Grant","raw_affiliation_strings":["Case Western Reserve University,Systems and Computer Engineering,Department of Electrical"],"affiliations":[{"raw_affiliation_string":"Case Western Reserve University,Systems and Computer Engineering,Department of Electrical","institution_ids":["https://openalex.org/I58956616"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024665967","display_name":"Peng Wang","orcid":"https://orcid.org/0000-0003-1602-8318"},"institutions":[{"id":"https://openalex.org/I58956616","display_name":"Case Western Reserve University","ror":"https://ror.org/051fd9666","country_code":"US","type":"education","lineage":["https://openalex.org/I58956616"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Peng Wang","raw_affiliation_strings":["Case Western Reserve University,Department of Mechanical and Aerospace Engineering,Cleveland,Ohio,USA"],"affiliations":[{"raw_affiliation_string":"Case Western Reserve University,Department of Mechanical and Aerospace Engineering,Cleveland,Ohio,USA","institution_ids":["https://openalex.org/I58956616"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5063784201"],"corresponding_institution_ids":["https://openalex.org/I58956616"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.36908014,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4240","last_page":"4247"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.10119999945163727,"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":0.10119999945163727,"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.08179999887943268,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T10812","display_name":"Human Pose and Action Recognition","score":0.08030000329017639,"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/quaternion","display_name":"Quaternion","score":0.9035000205039978},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5322999954223633},{"id":"https://openalex.org/keywords/rotation","display_name":"Rotation (mathematics)","score":0.48809999227523804},{"id":"https://openalex.org/keywords/quaternion-algebra","display_name":"Quaternion algebra","score":0.4672999978065491},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4666999876499176},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4456000030040741},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.44359999895095825},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43230000138282776}],"concepts":[{"id":"https://openalex.org/C200127275","wikidata":"https://www.wikidata.org/wiki/Q173853","display_name":"Quaternion","level":2,"score":0.9035000205039978},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6136999726295471},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5322999954223633},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5227000117301941},{"id":"https://openalex.org/C74050887","wikidata":"https://www.wikidata.org/wiki/Q848368","display_name":"Rotation (mathematics)","level":2,"score":0.48809999227523804},{"id":"https://openalex.org/C151640129","wikidata":"https://www.wikidata.org/wiki/Q2835967","display_name":"Quaternion algebra","level":5,"score":0.4672999978065491},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4666999876499176},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4478999972343445},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4456000030040741},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.44359999895095825},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43230000138282776},{"id":"https://openalex.org/C145941777","wikidata":"https://www.wikidata.org/wiki/Q5310237","display_name":"Dual quaternion","level":3,"score":0.4034999907016754},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.39079999923706055},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38830000162124634},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3666999936103821},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.36390000581741333},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.361299991607666},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34630000591278076},{"id":"https://openalex.org/C171036898","wikidata":"https://www.wikidata.org/wiki/Q256355","display_name":"Equivariant map","level":2,"score":0.3199999928474426},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.29420000314712524},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.28760001063346863},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.28380000591278076},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2533000111579895},{"id":"https://openalex.org/C61265191","wikidata":"https://www.wikidata.org/wiki/Q767770","display_name":"Scale-invariant feature transform","level":3,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros60139.2025.11247562","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros60139.2025.11247562","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2569680626","https://openalex.org/W2576915720","https://openalex.org/W2905556235","https://openalex.org/W2923153064","https://openalex.org/W2963177347","https://openalex.org/W2963230471","https://openalex.org/W2963299736","https://openalex.org/W2991363140","https://openalex.org/W3003215308","https://openalex.org/W3091664389","https://openalex.org/W3096609285","https://openalex.org/W3159481202","https://openalex.org/W3163462841","https://openalex.org/W4233520335","https://openalex.org/W4254719677","https://openalex.org/W4403417217"],"related_works":[],"abstract_inverted_index":{"This":[0,48],"paper":[1],"introduces":[2],"Quaternion":[3,64],"Approximate":[4],"Networks":[5,130],"(QUAN),":[6],"a":[7],"novel":[8],"deep":[9],"learning":[10],"framework":[11],"that":[12],"leverages":[13],"quaternion":[14,26,35,39,73,137],"algebra":[15],"for":[16,68,136,148],"rotation":[17,124],"equivariant":[18],"image":[19,83],"classification":[20,84,96],"and":[21,71,91,105,112,123,157],"object":[22,87],"detection.":[23],"Unlike":[24],"conventional":[25],"neural":[27],"networks":[28],"attempting":[29],"to":[30,75,109],"operate":[31],"entirely":[32],"in":[33,139,150,159],"the":[34,134],"domain,":[36],"QUAN":[37,79,98,118],"approximates":[38],"convolution":[40,111],"through":[41],"Hamilton":[42],"product":[43],"decomposition":[44],"using":[45],"real-valued":[46],"operations.":[47],"approach":[49],"preserves":[50],"geometric":[51],"properties":[52],"while":[53,132],"enabling":[54],"efficient":[55],"implementation":[56],"with":[57,102],"custom":[58],"CUDA":[59],"kernels.":[60],"We":[61],"introduce":[62],"Independent":[63],"Batch":[65],"Normalization":[66],"(IQBN)":[67],"training":[69],"stability":[70],"extend":[72],"operations":[74],"spatial":[76],"attention":[77],"mechanisms.":[78],"is":[80],"evaluated":[81],"on":[82],"(CIFAR-10/100,":[85],"ImageNet),":[86],"detection":[88],"(COCO,":[89],"DOTA),":[90],"robotic":[92,152],"perception":[93,156],"tasks.":[94],"In":[95],"tasks,":[97],"achieves":[99],"higher":[100],"accuracy":[101],"fewer":[103],"parameters":[104],"faster":[106],"convergence":[107],"compared":[108],"existing":[110],"quaternion-based":[113],"models.":[114],"For":[115],"objection":[116],"detection,":[117],"demonstrates":[119],"improved":[120],"parameter":[121],"efficiency":[122],"handling":[125],"over":[126],"standard":[127],"Convolutional":[128],"Neural":[129],"(CNNs)":[131],"establishing":[133],"SOTA":[135],"CNNs":[138],"this":[140],"downstream":[141],"task.":[142],"These":[143],"results":[144],"highlight":[145],"its":[146],"potential":[147],"deployment":[149],"resource-constrained":[151],"systems":[153],"requiring":[154],"rotation-aware":[155],"application":[158],"other":[160],"domains.":[161]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-11-28T00:00:00"}
