{"id":"https://openalex.org/W4414920251","doi":"https://doi.org/10.1145/3746027.3755355","title":"ST-SAM: SAM-Driven Self-Training Framework for Semi-Supervised Camouflaged Object Detection","display_name":"ST-SAM: SAM-Driven Self-Training Framework for Semi-Supervised Camouflaged Object Detection","publication_year":2025,"publication_date":"2025-10-25","ids":{"openalex":"https://openalex.org/W4414920251","doi":"https://doi.org/10.1145/3746027.3755355"},"language":"en","primary_location":{"id":"doi:10.1145/3746027.3755355","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3755355","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2507.23307","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102894788","display_name":"Xihang Hu","orcid":"https://orcid.org/0009-0004-8314-3740"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xihang Hu","raw_affiliation_strings":["Jilin University, Changchun, China"],"raw_orcid":"https://orcid.org/0009-0004-8314-3740","affiliations":[{"raw_affiliation_string":"Jilin University, Changchun, China","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014712849","display_name":"Fuming Sun","orcid":"https://orcid.org/0000-0003-3932-2712"},"institutions":[{"id":"https://openalex.org/I61565387","display_name":"Dalian Minzu University","ror":"https://ror.org/02hxfx521","country_code":"CN","type":"education","lineage":["https://openalex.org/I61565387"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fuming Sun","raw_affiliation_strings":["Dalian Minzu University, Dalian, China"],"raw_orcid":"https://orcid.org/0000-0003-3932-2712","affiliations":[{"raw_affiliation_string":"Dalian Minzu University, Dalian, China","institution_ids":["https://openalex.org/I61565387"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jiazhe Liu","orcid":"https://orcid.org/0009-0008-4668-7641"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiazhe Liu","raw_affiliation_strings":["Jilin University, Changchun, China"],"raw_orcid":"https://orcid.org/0009-0008-4668-7641","affiliations":[{"raw_affiliation_string":"Jilin University, Changchun, China","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103266430","display_name":"Feilong Xu","orcid":"https://orcid.org/0009-0001-7619-4564"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feilong Xu","raw_affiliation_strings":["Jilin University, Changchun, China"],"raw_orcid":"https://orcid.org/0009-0001-7619-4564","affiliations":[{"raw_affiliation_string":"Jilin University, Changchun, China","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"last","author":{"id":null,"display_name":"Xiaoli Zhang","orcid":"https://orcid.org/0000-0001-8412-4956"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoli Zhang","raw_affiliation_strings":["Jilin University, Changchun, China"],"raw_orcid":"https://orcid.org/0000-0001-8412-4956","affiliations":[{"raw_affiliation_string":"Jilin University, Changchun, China","institution_ids":["https://openalex.org/I194450716"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"8194","last_page":"8203"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9995999932289124,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9995999932289124,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9965000152587891,"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/T10971","display_name":"Olfactory and Sensory Function Studies","score":0.9926999807357788,"subfield":{"id":"https://openalex.org/subfields/2809","display_name":"Sensory Systems"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6234999895095825},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5945000052452087},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5212000012397766},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.48590001463890076},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4666999876499176},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4140999913215637},{"id":"https://openalex.org/keywords/mean-squared-prediction-error","display_name":"Mean squared prediction error","score":0.3249000012874603},{"id":"https://openalex.org/keywords/error-detection-and-correction","display_name":"Error detection and correction","score":0.3174000084400177}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8226000070571899},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6234999895095825},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5945000052452087},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5932000279426575},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5231000185012817},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5212000012397766},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.48590001463890076},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4666999876499176},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4140999913215637},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34619998931884766},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.3249000012874603},{"id":"https://openalex.org/C103088060","wikidata":"https://www.wikidata.org/wiki/Q1062839","display_name":"Error detection and correction","level":2,"score":0.3174000084400177},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.30889999866485596},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2971999943256378},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2827000021934509},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.2824000120162964},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2689000070095062},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.26570001244544983},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.265500009059906},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.25589999556541443},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.2556000053882599},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2524999976158142},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.25119999051094055},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.25060001015663147}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3746027.3755355","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3755355","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2507.23307","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.23307","pdf_url":"https://arxiv.org/pdf/2507.23307","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2507.23307","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.23307","pdf_url":"https://arxiv.org/pdf/2507.23307","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Semi-supervised":[0],"Camouflaged":[1],"Object":[2],"Detection":[3],"(SSCOD)":[4],"aims":[5],"to":[6,84,116],"reduce":[7],"reliance":[8],"on":[9,27,123,158],"costly":[10],"pixel-level":[11],"annotations":[12],"by":[13,96],"leveraging":[14],"limited":[15,50],"annotated":[16],"data":[17],"and":[18,35,49,80,142],"abundant":[19],"unlabeled":[20],"data.":[21],"However,":[22],"existing":[23,139],"SSCOD":[24,70,140],"methods":[25,141],"based":[26],"Teacher-Student":[28],"frameworks":[29],"suffer":[30],"from":[31,68],"severe":[32],"prediction":[33,93],"bias":[34],"error":[36,118],"propagation":[37],"under":[38],"scarce":[39],"supervision,":[40],"while":[41],"their":[42],"multi-network":[43],"architectures":[44],"incur":[45],"high":[46],"computational":[47],"overhead":[48],"scalability.":[51],"To":[52],"overcome":[53],"these":[54],"limitations,":[55],"we":[56],"propose":[57],"ST-SAM,":[58],"a":[59,86,153,167],"highly":[60],"annotation-efficient":[61,171],"yet":[62],"concise":[63],"framework":[64],"that":[65,77,128],"breaks":[66],"away":[67],"conventional":[69],"constraints.":[71],"Specifically,":[72],"ST-SAM":[73,105,129,149],"employs":[74],"Self-Training":[75],"strategy":[76],"dynamically":[78],"filters":[79],"expands":[81],"high-confidence":[82],"pseudo-labels":[83,98],"enhance":[85],"single-model":[87],"architecture,":[88],"thereby":[89],"fundamentally":[90],"circumventing":[91],"inter-model":[92],"bias.":[94],"Furthermore,":[95],"transforming":[97],"into":[99],"hybrid":[100],"prompts":[101],"containing":[102],"domain-specific":[103],"knowledge,":[104],"effectively":[106],"harnesses":[107],"the":[108],"Segment":[109],"Anything":[110],"Model's":[111],"potential":[112],"for":[113,170],"specialized":[114],"tasks":[115],"mitigate":[117],"accumulation":[119],"in":[120],"self-training.":[121],"Experiments":[122],"COD":[124],"benchmark":[125],"datasets":[126],"demonstrate":[127],"achieves":[130],"state-of-the-art":[131],"performance":[132],"with":[133],"only":[134,152],"1%":[135],"labeled":[136],"data,":[137],"outperforming":[138],"even":[143],"matching":[144],"fully":[145],"supervised":[146],"methods.":[147],"Remarkably,":[148],"requires":[150],"training":[151],"single":[154],"network,":[155],"without":[156],"relying":[157],"specific":[159],"models":[160],"or":[161],"loss":[162],"functions.":[163],"This":[164],"work":[165],"establishes":[166],"new":[168],"paradigm":[169],"SSCOD.":[172],"Codes":[173],"will":[174],"be":[175],"available":[176],"at":[177],"https://github.com/hu-xh/ST-SAM.":[178]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
