Visual motif identification: elaboration of a curated comparative dataset and classification methods
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- dc.contributor.author Phillips, Adam
- dc.contributor.author Grandes Rodriguez, Daniel
- dc.contributor.author Sánchez Manzano, Miriam
- dc.contributor.author Salvadó-Romero, Alan
- dc.contributor.author Garín Boronat, Manuel
- dc.contributor.author Haro Ortega, Gloria
- dc.contributor.author Ballester, Coloma
- dc.date.accessioned 2025-06-02T06:13:58Z
- dc.date.embargoEnd info:eu-repo/date/embargoEnd/2026-05-12
- dc.date.issued 2024
- dc.description.abstract In cinema, visual motifs are recurrent iconographic compositions that carry artistic or aesthetic significance. Their use throughout the history of visual arts and media is interesting to researchers and filmmakers alike. Our goal in this work is to recognise and classify these motifs by proposing a new machine learning model that uses a custom dataset to that end. We show how features extracted from a CLIP model can be leveraged by using a shallow network and an appropriate loss to classify images into 20 different motifs, with surprisingly good results: an F1-score of 0.91 on our test set. We also present several ablation studies justifying the input features, architecture and hyperparameters used.
- dc.description.sponsorship The authors acknowledge support by MICINN/FEDER UE project, ref. PID2021-127643NB-I00, and by Maria de Maeztu Units of Excellence Programme CEX2021-001195-M, funded by MICIU/AEI/10.13039/501100011033. They also thank the reviewers for their valuable comments and suggestions that helped to improve the manuscript.
- dc.embargo.liftdate 2026-05-12
- dc.format.mimetype application/pdf
- dc.identifier.citation Phillips A, Grandes Rodriguez D, Sánchez-Manzano M, Salvadó A, Garin M, Haro G, Ballester C. Visual motif identification: elaboration of a curated comparative dataset and classification methods. In: Leonardis A, Ricci E, Roth S, Russakovsky O, Sattler T, Varol G, editors. 18th European Conference on Computer Vision Part VI (ECCV 2024); 2024 Sept 29 - October 4; Milan, Italy. Cham: Springer Verlag; 2024. p.341-61. (LNCS; no. 15628). DOI: 10.1007/978-3-031-91572-7_21
- dc.identifier.uri http://hdl.handle.net/10230/70580
- dc.language.iso eng
- dc.publisher Springer
- dc.relation.projectID info:eu-repo/grantAgreement/ES/3PE/PID2021-127643
- dc.rights © 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG
- dc.rights.accessRights info:eu-repo/semantics/embargoedAccess
- dc.subject.keyword Visual motif
- dc.subject.keyword Dataset
- dc.subject.keyword Deep learning based models
- dc.subject.keyword CLIP features
- dc.subject.keyword Recognition and classification
- dc.title Visual motif identification: elaboration of a curated comparative dataset and classification methods
- dc.type info:eu-repo/semantics/conferenceObject
- dc.type.version info:eu-repo/semantics/acceptedVersion