{"id":"https://openalex.org/W4415708182","doi":"https://doi.org/10.1109/icme59968.2025.11210177","title":"Infrared Small Target Detection via Multi-Path Deep Conduction","display_name":"Infrared Small Target Detection via Multi-Path Deep Conduction","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4415708182","doi":"https://doi.org/10.1109/icme59968.2025.11210177"},"language":null,"primary_location":{"id":"doi:10.1109/icme59968.2025.11210177","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11210177","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Multimedia and Expo (ICME)","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/A5070122639","display_name":"Yongji Li","orcid":"https://orcid.org/0000-0002-2212-7673"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongji Li","raw_affiliation_strings":["Sun Yat-Sen University,School of Electronics and Communication Engineering,Shenzhen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University,School of Electronics and Communication Engineering,Shenzhen,China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100748064","display_name":"Luping Wang","orcid":"https://orcid.org/0000-0003-4119-4799"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Luping Wang","raw_affiliation_strings":["Sun Yat-Sen University,School of Electronics and Communication Engineering,Shenzhen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University,School of Electronics and Communication Engineering,Shenzhen,China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.39509426,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9240999817848206,"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/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9240999817848206,"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":0.013199999928474426,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.012400000356137753,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/benchmark","display_name":"Benchmark (surveying)","score":0.6984000205993652},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.6909999847412109},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6653000116348267},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4830000102519989},{"id":"https://openalex.org/keywords/phase","display_name":"Phase (matter)","score":0.4717000126838684},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.47099998593330383}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6984000205993652},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.6909999847412109},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6653000116348267},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6040999889373779},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6011999845504761},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4830000102519989},{"id":"https://openalex.org/C44280652","wikidata":"https://www.wikidata.org/wiki/Q104837","display_name":"Phase (matter)","level":2,"score":0.4717000126838684},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.47099998593330383},{"id":"https://openalex.org/C158355884","wikidata":"https://www.wikidata.org/wiki/Q11388","display_name":"Infrared","level":2,"score":0.446399986743927},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.34790000319480896},{"id":"https://openalex.org/C172100665","wikidata":"https://www.wikidata.org/wiki/Q7465774","display_name":"Thermal conduction","level":2,"score":0.3312000036239624},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3292999863624573},{"id":"https://openalex.org/C180205008","wikidata":"https://www.wikidata.org/wiki/Q159190","display_name":"Amplitude","level":2,"score":0.3156999945640564},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.30379998683929443},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2639000117778778},{"id":"https://openalex.org/C43571822","wikidata":"https://www.wikidata.org/wiki/Q599037","display_name":"Near-infrared spectroscopy","level":2,"score":0.2623000144958496}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme59968.2025.11210177","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11210177","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W2049769803","https://openalex.org/W2774646044","https://openalex.org/W2991758591","https://openalex.org/W3086144474","https://openalex.org/W3088317060","https://openalex.org/W3110352253","https://openalex.org/W3124866053","https://openalex.org/W3131633592","https://openalex.org/W3138516171","https://openalex.org/W3171950886","https://openalex.org/W3212966035","https://openalex.org/W4226043641","https://openalex.org/W4285259796","https://openalex.org/W4313506322","https://openalex.org/W4313549920","https://openalex.org/W4317794940","https://openalex.org/W4320005476","https://openalex.org/W4366668641","https://openalex.org/W4372267081","https://openalex.org/W4383813293","https://openalex.org/W4386158776","https://openalex.org/W4391376377","https://openalex.org/W4394597260","https://openalex.org/W4394706539","https://openalex.org/W4396887831","https://openalex.org/W4402159374","https://openalex.org/W4402979051","https://openalex.org/W4402982192"],"related_works":[],"abstract_inverted_index":{"Infrared":[0],"small":[1,69,86,100],"target":[2],"detection":[3],"(IRSTD)":[4],"in":[5],"heterogeneous":[6],"scenes":[7],"remains":[8],"a":[9,16],"challenging":[10],"task.":[11],"To":[12,35],"tackle":[13],"this":[14],"issue,":[15],"Multi-Path":[17],"Deep":[18],"Conduction":[19],"Network":[20],"(MPDCNet)":[21],"is":[22],"proposed.":[23],"Three":[24],"independent":[25],"branch":[26],"networks":[27],"actively":[28],"explore":[29],"spatial,":[30],"amplitude,":[31],"and":[32,55,76,82,104],"phase":[33],"features.":[34],"facilitate":[36],"the":[37,42,50,58,65,92,94,99],"integration":[38],"of":[39,53,68,74,85],"different":[40],"features,":[41],"proposed":[43,110],"Amplitude-Phase":[44],"Interactive":[45],"Module":[46,62],"(APIM)":[47],"efficiently":[48],"learns":[49],"distributional":[51],"features":[52,67],"amplitude":[54],"phase.":[56],"Furthermore,":[57],"engineered":[59],"Spatial-Frequency":[60],"Interaction":[61],"(SFIM)":[63],"enhances":[64],"integrated":[66],"targets":[70,87],"through":[71],"interactive":[72],"learning":[73],"spectral":[75],"spatial":[77],"feature":[78],"maps.":[79],"The":[80],"high-frequency":[81],"salient":[83],"attributes":[84],"are":[88],"augmented":[89],"simultaneously.":[90],"In":[91],"end,":[93],"well-configured":[95],"decoder":[96],"accurately":[97],"locates":[98],"targets.":[101],"Both":[102],"qualitative":[103],"quantitative":[105],"experiments":[106],"demonstrate":[107],"that":[108],"our":[109],"MPDCNet":[111],"outperforms":[112],"14":[113],"recent":[114],"benchmark":[115],"algorithms":[116],"on":[117],"two":[118],"public":[119],"datasets.":[120]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-30T00:00:00"}