Performance Analysis of Image Caption Generation Techniques Using CNN-Based Encoder–Decoder Architecture
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Publisher
Springer Nature Singapore
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https://link.springer.com/content/pdf/10.1007/978-981-99-6755-1_23
Reference16 articles.
1. Vinyals O, Toshev A, Bengio S, Erhan D (2015) Show and tell: a neural image caption generator. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3156–3164. https://doi.org/10.1109/CVPR.2015.7298935
2. Jethwa N, Gabajiwala H, Mishra A, Joshi P, Natu P (2021) Comparative analysis between InceptionResNetV2 and InceptionV3 for attention based image captioning. In: 2021 2nd global conference for advancement in technology (GCAT), pp. 1–6. Bangalore, India. https://doi.org/10.1109/GCAT52182.2021.9587514
3. Alam MS, Rahman MS, Hosen MI, Mubin KA, Hossen S, Mridha MF (2021) Comparison of Different CNN Model used as Encoders for Image Captioning. In: 2021 International Conference on Data Analytics for Business and Industry (ICDABI), pp. 523–526. Sakheer, Bahrain. https://doi.org/10.1109/ICDABI53623.2021.9655846
4. Suresh, K. R., Jarapala, A., & Sudeep, P. V.: Image Captioning Encoder–Decoder Models Using CNN-RNN Architectures: A Comparative Study. Circuits, Systems, and Signal Processing, pp. 5719–5742. https://doi.org/10.1007/s00034-022-02050-2 (2022)
5. Kanimozhiselvi, C. S., V, K., P, K. S., & S, K.: Image Captioning Using Deep Learning. In: 2022 International Conference on Computer Communication and Informatics (ICCCI), pp. 1–7. Coimbatore, India. https://doi.org/10.1109/ICCCI54379.2022.9740788 (2022)
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