{"id":"https://openalex.org/W7129651839","doi":"https://doi.org/10.1109/icipw68931.2025.11386341","title":"Tie-Breaking Conflict-Ease Cross-Modal Hashing","display_name":"Tie-Breaking Conflict-Ease Cross-Modal Hashing","publication_year":2025,"publication_date":"2025-09-14","ids":{"openalex":"https://openalex.org/W7129651839","doi":"https://doi.org/10.1109/icipw68931.2025.11386341"},"language":null,"primary_location":{"id":"doi:10.1109/icipw68931.2025.11386341","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icipw68931.2025.11386341","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 Image Processing Workshops (ICIPW)","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/A5081724365","display_name":"Changchang Sun","orcid":"https://orcid.org/0000-0003-3685-1205"},"institutions":[{"id":"https://openalex.org/I39422238","display_name":"University of Illinois Chicago","ror":"https://ror.org/02mpq6x41","country_code":"US","type":"education","lineage":["https://openalex.org/I39422238"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Changchang Sun","raw_affiliation_strings":["University of Illinois,Chicago"],"affiliations":[{"raw_affiliation_string":"University of Illinois,Chicago","institution_ids":["https://openalex.org/I39422238"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126212428","display_name":"Jialie Shen","orcid":null},"institutions":[{"id":"https://openalex.org/I4210160447","display_name":"Universidad de Londres","ror":"https://ror.org/054v4yq51","country_code":"MX","type":"education","lineage":["https://openalex.org/I4210160447"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Jialie Shen","raw_affiliation_strings":["University of London"],"affiliations":[{"raw_affiliation_string":"University of London","institution_ids":["https://openalex.org/I4210160447"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126267968","display_name":"Gaowen Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gaowen Liu","raw_affiliation_strings":["Cisco Research"],"affiliations":[{"raw_affiliation_string":"Cisco Research","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122887395","display_name":"Aihua Zheng","orcid":null},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Aihua Zheng","raw_affiliation_strings":["Anhui University"],"affiliations":[{"raw_affiliation_string":"Anhui University","institution_ids":["https://openalex.org/I143868143"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5126190329","display_name":"Yan Yan","orcid":null},"institutions":[{"id":"https://openalex.org/I39422238","display_name":"University of Illinois Chicago","ror":"https://ror.org/02mpq6x41","country_code":"US","type":"education","lineage":["https://openalex.org/I39422238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yan Yan","raw_affiliation_strings":["University of Illinois,Chicago"],"affiliations":[{"raw_affiliation_string":"University of Illinois,Chicago","institution_ids":["https://openalex.org/I39422238"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5081724365"],"corresponding_institution_ids":["https://openalex.org/I39422238"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.74350725,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"149","last_page":"154"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.8364999890327454,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.8364999890327454,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.062300000339746475,"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/T11448","display_name":"Face recognition and analysis","score":0.007699999958276749,"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/similarity","display_name":"Similarity (geometry)","score":0.6266999840736389},{"id":"https://openalex.org/keywords/semantic-similarity","display_name":"Semantic similarity","score":0.5425999760627747},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.5418999791145325},{"id":"https://openalex.org/keywords/hash-function","display_name":"Hash function","score":0.49549999833106995},{"id":"https://openalex.org/keywords/locality-sensitive-hashing","display_name":"Locality-sensitive hashing","score":0.4934000074863434},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.4763999879360199},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.4099000096321106},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.4011000096797943},{"id":"https://openalex.org/keywords/nearest-neighbor-search","display_name":"Nearest neighbor search","score":0.3783999979496002}],"concepts":[{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.6266999840736389},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5961999893188477},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.5425999760627747},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.5418999791145325},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5101000070571899},{"id":"https://openalex.org/C99138194","wikidata":"https://www.wikidata.org/wiki/Q183427","display_name":"Hash function","level":2,"score":0.49549999833106995},{"id":"https://openalex.org/C74270461","wikidata":"https://www.wikidata.org/wiki/Q1625299","display_name":"Locality-sensitive hashing","level":4,"score":0.4934000074863434},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.4763999879360199},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4636000096797943},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.4099000096321106},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.4011000096797943},{"id":"https://openalex.org/C116738811","wikidata":"https://www.wikidata.org/wiki/Q608751","display_name":"Nearest neighbor search","level":2,"score":0.3783999979496002},{"id":"https://openalex.org/C63435697","wikidata":"https://www.wikidata.org/wiki/Q864135","display_name":"Binary code","level":3,"score":0.3668999969959259},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36559998989105225},{"id":"https://openalex.org/C2779494224","wikidata":"https://www.wikidata.org/wiki/Q5645799","display_name":"Hamming space","level":5,"score":0.3481000065803528},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3402000069618225},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.33660000562667847},{"id":"https://openalex.org/C193319292","wikidata":"https://www.wikidata.org/wiki/Q272172","display_name":"Hamming distance","level":2,"score":0.33079999685287476},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32829999923706055},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.3089999854564667},{"id":"https://openalex.org/C2779190172","wikidata":"https://www.wikidata.org/wiki/Q4913888","display_name":"Binary data","level":3,"score":0.2980000078678131},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.29269999265670776},{"id":"https://openalex.org/C143271835","wikidata":"https://www.wikidata.org/wiki/Q254515","display_name":"Similitude","level":2,"score":0.2872999906539917},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27880001068115234},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.26080000400543213},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icipw68931.2025.11386341","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icipw68931.2025.11386341","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 Image Processing Workshops (ICIPW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6297441124916077,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1969752030","https://openalex.org/W1976258951","https://openalex.org/W1996219872","https://openalex.org/W2007972815","https://openalex.org/W2084363474","https://openalex.org/W2086958058","https://openalex.org/W2087193308","https://openalex.org/W2155803963","https://openalex.org/W2159373756","https://openalex.org/W2203543769","https://openalex.org/W2266728343","https://openalex.org/W2591669147","https://openalex.org/W2890043419","https://openalex.org/W2954487216","https://openalex.org/W2963187862","https://openalex.org/W2963305974","https://openalex.org/W2981777726","https://openalex.org/W4282926067","https://openalex.org/W4304092117","https://openalex.org/W4382239230"],"related_works":[],"abstract_inverted_index":{"Most":[0],"existing":[1,81],"deep":[2],"supervised":[3],"cross-modal":[4,18,114],"hashing":[5,19,115],"approaches":[6],"leverage":[7],"a":[8,110],"pairwise":[9],"binary":[10,84,156],"similarity":[11,24,41,85,124,139,157],"matrix":[12,158],"as":[13,159],"supervision":[14],"to":[15,49,153],"optimize":[16],"their":[17],"models":[20],"coarsely.":[21],"Unfortunately,":[22],"such":[23],"modeling":[25,86],"strategy":[26],"rarely":[27],"takes":[28],"the":[29,40,52,64,71,77,89,97,120,130,137,146,168],"tie-breaking":[30],"into":[31],"account":[32],"for":[33],"data":[34,44,143],"annotated":[35],"with":[36,136],"multi-labels":[37],"and":[38,46,57,102,112],"treats":[39],"relationship":[42,54,72,140],"between":[43],"equally":[45],"indiscriminately,":[47],"failing":[48],"truly":[50],"characterize":[51],"semantic":[53,123],"among":[55],"instances":[56],"resulting":[58],"in":[59,63,76,129,141],"bad":[60],"ranking":[61],"performance":[62],"practical":[65],"system.":[66],"Besides,":[67],"we":[68,108],"observe":[69],"that":[70],"conflict":[73],"naturally":[74],"exists":[75],"training":[78],"phase":[79],"of":[80,170],"methods":[82,154],"utilizing":[83],"strategy,":[87],"where":[88,126],"model":[90,147],"convergence":[91],"efficiency":[92,149],"is":[93,100,133],"badly":[94],"affected":[95],"since":[96],"optimization":[98,148],"goal":[99],"unclear":[101],"globally":[103],"inconsistent.":[104],"In":[105],"this":[106],"paper,":[107],"propose":[109],"Tie-breAking":[111],"ConflicT-ease":[113],"method":[116,172],"(TACT)":[117],"by":[118],"exploring":[119],"multi-label":[121],"oriented":[122],"relationship,":[125],"modality":[127],"distribution":[128],"Hamming":[131],"space":[132],"more":[134],"consistent":[135],"actual":[138],"original":[142],"space.":[144],"Meanwhile,":[145],"increases":[150],"accordingly":[151],"compared":[152],"using":[155],"supervision.":[160],"Extensive":[161],"experiments":[162],"on":[163],"two":[164],"benchmark":[165],"datasets":[166],"demonstrate":[167],"superiority":[169],"our":[171],"over":[173],"several":[174],"state-of-the-art":[175],"baselines.":[176]},"counts_by_year":[],"updated_date":"2026-02-19T06:27:42.648592","created_date":"2026-02-18T00:00:00"}