Exploring the Synergy Between Vision-Language Pretraining and ChatGPT for Artwork Captioning: A Preliminary Study
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Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-51026-7_27
Reference31 articles.
1. Aslan, S., Castellano, G., Digeno, V., Migailo, G., Scaringi, R., Vessio, G.: Recognizing the emotions evoked by artworks through visual features and knowledge graph-embeddings. In: Mazzeo, P.L., Frontoni, E., Sclaroff, S., Distante, C. (eds.) Image Analysis and Processing. ICIAP 2022. LNCS, vol. 13373, pp. 129–140. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-13321-3_12
2. Bai, Z., Nakashima, Y., Garcia, N.: Explain me the painting: multi-topic knowledgeable art description generation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5422–5432 (2021)
3. Becattini, F., et al.: VISCOUNTH: a large-scale multilingual visual question answering dataset for cultural heritage. ACM Trans. Multim. Comput. Commun. Appl. (2023). Just Accepted
4. Bongini, P., Becattini, F., Del Bimbo, A.: Is GPT-3 all you need for visual question answering in cultural heritage? In: Karlinsky, L., Michaeli, T., Nishino, K. (eds.) ECCV 2022. LNCS, vol. 13801, pp. 268–281. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-25056-9_18
5. Castellano, G., Digeno, V., Sansaro, G., Vessio, G.: Leveraging knowledge graphs and deep learning for automatic art analysis. Knowl.-Based Syst. 248, 108859 (2022)
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