{"doi":"10.1109/tcsvt.2022.3177320","title":"Video Captioning Using Global-Local Representation","abstract":"Video captioning is a challenging task as it needs to accurately transform visual understanding into natural language description. To date, state-of-the-art methods inadequately model global-local vision representation for sentence generation, leaving plenty of room for improvement. In this work, we approach the video captioning task from a new perspective and propose a GLR framework, namely a global-local representation granularity. Our GLR demonstrates three advantages over the prior efforts. First, we propose a simple solution, which exploits extensive vision representations from different video ranges to improve linguistic expression. Second, we devise a novel global-local encoder, which encodes different video representations including long-range, short-range and local-keyframe, to produce rich semantic vocabulary for obtaining a descriptive granularity of video contents across frames. Finally, we introduce the progressive training strategy which can effectively organize feature learning to incur optimal captioning behavior. Evaluated on the MSR-VTT and MSVD dataset, we outperform recent state-of-the-art methods including a well-tuned SA-LSTM baseline by a significant margin, with shorter training schedules. Because of its simplicity and efficacy, we hope that our GLR could serve as a strong baseline for many video understanding tasks besides video captioning. Code will be available.","journal":"IEEE Transactions on Circuits and Systems for Video Technology","year":2022,"id":234027,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":109,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9557,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":849140,"name":"Siqi Ma","orcid":"0000-0003-3479-5713","position":1,"is_corresponding":false},{"id":849141,"name":"Qifan Wang","orcid":"0000-0002-7570-5756","position":2,"is_corresponding":false},{"id":849142,"name":"Yingjie Chen","orcid":"0000-0001-6705-3535","position":3,"is_corresponding":false},{"id":849143,"name":"Xiangyu Zhang","orcid":"0000-0002-9544-2500","position":4,"is_corresponding":false},{"id":849144,"name":"Andreas Savakis","orcid":"0000-0002-9657-3027","position":5,"is_corresponding":false},{"id":778009,"name":"Dongfang Liu","orcid":"0000-0001-6995-4775","position":6,"is_corresponding":false},{"id":849139,"name":"Liqi Yan","orcid":"0000-0002-7077-4947","position":0,"is_corresponding":true}],"reference_count":96,"raw_metadata":null,"created_at":"2026-07-19T00:21:36.529874Z","pmid":"37215187","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":[]}