On combining image features and word embeddings for image captioning

• Autorzy: Bartosiewicz Mateusz , Iwanowski Marcin , Wiszniewska Martika , Frączak Karolina , Leśnowolski Paweł

Identyfikatory

DOI

10.15439/2023F997

• Języki publikacji: EN

Abstrakty

EN

Image captioning is the task of generating semantically and grammatically correct caption for a given image. Captioning model usually has an encoder-decoder structure where encoded image is decoded as list of words being a consecutive elements of the descriptive sentence. In this work, we investigate how encoding of the input image and way of coding words affects the result of the training of the encoder-decoder captioning model. We performed experiments with image encoding using 10 all-purpose popular backbones and 2 types of word embeddings. We compared those models using most popular image captioning evaluation metrics. Our research shows that the model's performance highly depends on the optimal combination of the neural image feature extractor and language processing model. The outcome of our research are applicable in all the research works that lead to the developing the optimal encoder- decoder image captioning model.

Słowa kluczowe

EN

image caption; neural image feature extractors; embedding models; LSTM

PL

podpis obrazu; ekstrakcja neuronowa; modele osadzania; LSTM; ekstrakcja cech obrazu

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2023
• Tom: Vol. 35
• Strony: 355--365
• Opis fizyczny: Bibliogr. 59 poz., il., tab.

Twórcy

autor

Bartosiewicz Mateusz

• Institute of Control and Industrial Electronics, Warsaw University of Technology ul. Koszykowa 75; 00-662 Warszawa Poland

autor

Iwanowski Marcin

• Institute of Control and Industrial Electronics, Warsaw University of Technology ul. Koszykowa 75; 00-662 Warszawa Poland

autor

Wiszniewska Martika

• Institute of Control and Industrial Electronics, Warsaw University of Technology ul. Koszykowa 75; 00-662 Warszawa Poland

autor

Frączak Karolina

• Institute of Control and Industrial Electronics, Warsaw University of Technology ul. Koszykowa 75; 00-662 Warszawa Poland

autor

Leśnowolski Paweł

• Institute of Control and Industrial Electronics, Warsaw University of Technology ul. Koszykowa 75; 00-662 Warszawa Poland

Bibliografia

• 1. P. Anderson, B. Fernando, M. Johnson, and S. Gould. Spice: Semantic propositional image caption evaluation. In European conference on computer vision, pages 382–398. Springer, 2016.
• 2. P. Anderson, X. He, C. Buehler, D. Teney, M. Johnson, S. Gould, and L. Zhang. Bottom-up and top-down attention for image captioning and visual question answering. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6077–6086, 2018.
• 3. S. Banerjee and A. Lavie. Meteor: An automatic metric for mt evaluation with improved correlation with human judgments. In IEE-valuation@ACL, 2005.
• 4. P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov. Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics, 5:135–146, 2017.
• 5. X. Chen, H. Fang, T. Lin, R. Vedantam, S. Gupta, P. Dollár, and C. L. Zitnick. Microsoft COCO captions: Data collection and evaluation server. CoRR, abs/1504.00325, 2015.
• 6. F. Chollet. Xception: Deep learning with depthwise separable convolutions. CoRR, abs/1610.02357, 2016.
• 7. Y. Cui, G. Yang, A. Veit, X. Huang, and S. Belongie. Learning to evaluate image captioning. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5804–5812, Los Alamitos, CA, USA, jun 2018. IEEE Computer Society.
• 8. J. Delbrouck and S. Dupont. Bringing back simplicity and lightliness into neural image captioning. CoRR, abs/1810.06245, 2018.
• 9. J. Donahue, L. A. Hendricks, M. Rohrbach, S. Venugopalan, S. Guadarrama, K. Saenko, and T. Darrell. Long-term recurrent convolutional networks for visual recognition and description. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4):677–691, 2017.
• 10. H. Dong, J. Zhang, D. McIlwraith, and Y. Guo. I2t2i: Learning text to image synthesis with textual data augmentation. In 2017 IEEE International Conference on Image Processing (ICIP), page 2015–2019. IEEE Press, 2017.
• 11. H. Fang, S. Gupta, F. Iandola, R. K. Srivastava, L. Deng, P. Dollar, J. Gao, X. He, M. Mitchell, J. C. Platt, C. Lawrence Zitnick, and G. Zweig. From captions to visual concepts and back. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015.
• 12. A. Farhadi, M. Hejrati, M. A. Sadeghi, P. Young, C. Rashtchian, J. Hockenmaier, and D. Forsyth. Every picture tells a story: Generating sentences from images. In K. Daniilidis, P. Maragos, and N. Paragios, editors, Computer Vision – ECCV 2010, pages 15–29, Berlin, Heidelberg, 2010. Springer Berlin Heidelberg.
• 13. R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In 2014 IEEE Conference on Computer Vision and Pattern Recognition, pages 580–587, 2014.
• 14. J. Gu, G. Wang, J. Cai, and T. Chen. An empirical study of language cnn for image captioning. 2017 IEEE International Conference on Computer Vision (ICCV), pages 1231–1240, 2016.
• 15. L. Guo, J. Liu, J. Tang, J. Li, W. Luo, and H. Lu. Aligning linguistic words and visual semantic units for image captioning. In Proceedings of the 27th ACM International Conference on Multimedia, MM ’19, page 765–773, New York, NY, USA, 2019. Association for Computing Machinery.
• 16. K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770–778, 2016.
• 17. S. Hochreiter. The vanishing gradient problem during learning recurrent neural nets and problem solutions. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 6:107–116, 04 1998.
• 18. S. Hochreiter and J. Schmidhuber. Lstm long short-term memory. Neural computation, 9:1735–80, 12 1997.
• 19. M. Hodosh, P. Young, and J. Hockenmaier. Framing image description as a ranking task: Data, models and evaluation metrics. Journal of Artificial Intelligence Research, 47:853–899, 05 2013.
• 20. A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam. Mobilenets: Efficient convolutional neural networks for mobile vision applications. CoRR, abs/1704.04861, 2017.
• 21. G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger. Densely connected convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017.
• 22. A. Janusz, D. Kałuża, M. Matraszek, Łukasz Grad, M. Świechowski, and D. Ślezak. Learning multimodal entity representations and their ensembles, with applications in a data-driven advisory framework for video game players. Information Sciences, 617:193–210, 2022.
• 23. J. Johnson, A. Karpathy, and L. Fei-Fei. Densecap: Fully convolutional localization networks for dense captioning. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4565–4574, 2016.
• 24. A. Karpathy and L. Fei-Fei. Deep visual-semantic alignments for generating image descriptions. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3128–3137, 2015.
• 25. R. Kiros, R. Salakhutdinov, and R. Zemel. Multimodal neural language models. In E. P. Xing and T. Jebara, editors, Proceedings of the 31st International Conference on Machine Learning, number 2 in Proceedings of Machine Learning Research, pages 595–603, Bejing, China, 22–24 Jun 2014. PMLR.
• 26. M. J. Kusner, Y. Sun, N. I. Kolkin, and K. Q. Weinberger. From word embeddings to document distances. In ICML, 2015.
• 27. P. Kuznetsova, V. Ordonez, A. Berg, T. Berg, and Y. Choi. Collective generation of natural image descriptions. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 359–368, Jeju Island, Korea, July 2012. Association for Computational Linguistics.
• 28. R. Lebret, P. O. Pinheiro, and R. Collobert. Phrase-based image captioning. In Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37, ICML’15, page 2085–2094. JMLR.org, 2015.
• 29. S. Li, G. Kulkarni, T. L. Berg, A. C. Berg, and Y. Choi. Composing simple image descriptions using web-scale n-grams. In Proceedings of the Fifteenth Conference on Computational Natural Language Learning, pages 220–228, Portland, Oregon, USA, June 2011. Association for Computational Linguistics.
• 30. C.-Y. Lin. ROUGE: A package for automatic evaluation of summaries. In Text Summarization Branches Out, pages 74–81, Barcelona, Spain, July 2004. Association for Computational Linguistics.
• 31. T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick. Microsoft coco: Common objects in context. In D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, editors, Computer Vision – ECCV 2014, pages 740–755, Cham, 2014. Springer International Publishing.
• 32. S. Liu, L. Bai, Y. Hu, and H. Wang. Image captioning based on deep neural networks. MATEC Web of Conferences, 232:01052, 11 2018.
• 33. J. Lu, C. Xiong, D. Parikh, and R. Socher. Knowing when to look: Adaptive attention via a visual sentinel for image captioning. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3242–3250, 2017.
• 34. J. Mao, W. Xu, Y. Yang, J. Wang, and A. L. Yuille. Explain images with multimodal recurrent neural networks. CoRR, abs/1410.1090, 2014.
• 35. T. Mikolov, K. Chen, G. S. Corrado, and J. Dean. Efficient estimation of word representations in vector space. In International Conference on Learning Representations, 2013.
• 36. M. Mitchell, X. Han, J. Dodge, A. Mensch, A. Goyal, A. Berg, K. Yamaguchi, T. Berg, K. Stratos, and H. Daumé. Midge: Generating image descriptions from computer vision detections. In Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, EACL ’12, page 747–756, USA, 2012. Association for Computational Linguistics.
• 37. V. Nair and G. E. Hinton. Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th International Conference on International Conference on Machine Learning, ICML’10, page 807–814, Madison, WI, USA, 2010. Omnipress.
• 38. K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu. Bleu: A method for automatic evaluation of machine translation. In Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, ACL ’02, page 311–318, USA, 2002. Association for Computational Linguistics.
• 39. J. Pennington, R. Socher, and C. D. Manning. Glove: Global vectors for word representation. In Empirical Methods in Natural Language Processing (EMNLP), pages 1532–1543, 2014.
• 40. D. Ramachandram and G. W. Taylor. Deep multimodal learning: A survey on recent advances and trends. IEEE Signal Processing Magazine, 34(6):96–108, 2017.
• 41. S. J. Rennie, E. Marcheret, Y. Mroueh, J. Ross, and V. Goel. Self-critical sequence training for image captioning. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1179–1195, 2017.
• 42. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. FeiFei. Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115(3):211–252, Dec 2015.
• 43. A. B. Sai, A. K. Mohankumar, and M. M. Khapra. A survey of evaluation metrics used for NLG systems. CoRR, abs/2008.12009, 2020.
• 44. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In Y. Bengio and Y. LeCun, editors, 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, 2015.
• 45. R. Subash, R. Jebakumar, Y. Kamdar, and N. Bhatt. Automatic image captioning using convolution neural networks and lstm. Journal of Physics: Conference Series, 1362(1):012096, nov 2019.
• 46. Y. Sugano and A. Bulling. Seeing with humans: Gaze-assisted neural image captioning. ArXiv, abs/1608.05203, 2016.
• 47. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1–9, 2015.
• 48. C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna. Rethinking the inception architecture for computer vision. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2818–2826, 2016.
• 49. M. Toshevska, F. Stojanovska, E. Zdravevski, P. Lameski, and S. Gievska. Exploration into deep learning text generation architectures for dense image captioning. In 2020 15th Conference on Computer Science and Information Systems (FedCSIS), pages 129–136, 2020.
• 50. R. Vedantam, C. L. Zitnick, and D. Parikh. Cider: Consensus-based image description evaluation. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4566–4575, 2015.
• 51. O. Vinyals, A. Toshev, S. Bengio, and D. Erhan. Show and tell: A neural image caption generator. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3156–3164, 2015.
• 52. S.-S. Wang and R.-Y. Dong. Learning complex spatial relation model from spatial data. Journal of Computers, 30(6):123–136, 2019.
• 53. Y. Xian and Y. Tian. Self-guiding multimodal lstm-when we do not have a perfect training dataset for image captioning. IEEE Transactions on Image Processing, PP, 09 2017.
• 54. X. Xiao, L. Wang, K. Ding, S. Xiang, and C. Pan. Dense semantic embedding network for image captioning. Pattern Recognition, 90:285–296, 2019.
• 55. K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhudinov, R. Zemel, and Y. Bengio. Show, attend and tell: Neural image caption generation with visual attention. In F. Bach and D. Blei, editors, Proceedings of the 32nd International Conference on Machine Learning, volume 37 of Proceedings of Machine Learning Research, pages 2048–2057, Lille, France, 07–09 Jul 2015. PMLR.
• 56. K. Xu, H. Wang, and P. Tang. Image captioning with deep lstm based on sequential residual. In 2017 IEEE International Conference on Multimedia and Expo (ICME), pages 361–366, 2017.
• 57. N. Xu, A. Liu, J. Liu, W. Nie, and Y. Su. Scene graph captioner: Image captioning based on structural visual representation. J. Vis. Commun. Image Represent., 58:477–485, 2019.
• 58. P. Young, A. Lai, M. Hodosh, and J. Hockenmaier. From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions. Transactions of the Association for Computational Linguistics, 2:67–78, 2014.
• 59. X. Zhang, S. He, X. Song, R. W. Lau, J. Jiao, and Q. Ye. Image captioning via semantic element embedding. Neurocomputing, 395:212–221, 2020.

Uwagi

1. Thematic Tracks Regular Papers

2. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).