{"doi":"10.1109/ispds56360.2022.9874232","title":"Improved Few-Shot Learning for Images Classification","abstract":null,"journal":"2022 3rd International Conference on Information Science, Parallel and Distributed Systems (ISPDS)","year":2022,"id":592383,"datarank":0.17123198430275366,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.006440141002537194,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.006440141002537194,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":2,"citers_with_citation_signal":1,"citers_with_endowment":1,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":135286,"name":"Jun Liang","orcid":"0000-0002-8105-2448","position":1,"is_corresponding":false},{"id":1515793,"name":"Haoyang Mei","orcid":null,"position":2,"is_corresponding":false},{"id":997495,"name":"Jingwen Fan","orcid":"0000-0001-5713-9722","position":3,"is_corresponding":false},{"id":1515797,"name":"Songsen Yu","orcid":null,"position":4,"is_corresponding":false},{"id":170771,"name":"Jialin Yu","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Improved Few-Shot Learning for Images Classification","abstract":"Few-shot learning is an approach that classify unseen classes with limited labeled samples. We propose improved networks of Relation Network to classify images with small samples. The improved networks is ECA Relation Network (ECA-RNET). The accuracy of ECA-RNET is 52.24% and 67.85% on 5-way 1-shot and 5-way 5-shot of mini-ImageNet dataset, respectively.","is_dataset_classified":null,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"26657633","pmcid":null,"openalex_id":"https://openalex.org/W4294892107","authors":[],"funders":[],"total_grants":0,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[{"year":2024,"count":1},{"year":2025,"count":1}],"oa_status":"closed","license":"https://doi.org/10.15223/policy-029","oa_locations":[{"url":"http://xplorestaging.ieee.org/ielx7/9873852/9874003/09874232.pdf?arnumber=9874232","host_type":"publisher"},{"url":"https://doi.org/10.1109/ispds56360.2022.9874232","host_type":""}],"fields_of_study":["Domain Adaptation and Few-Shot Learning","Machine Learning and ELM","COVID-19 diagnosis using AI"],"mesh_terms":[],"keywords":["Shot (pellet)","Artificial intelligence","One shot","Computer science","Relation (database)","Pattern recognition (psychology)","Single shot","Contextual image classification","Image (mathematics)","Machine learning","Data mining","Engineering","Physics","Optics"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-26T13:25:52.479140Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}