Abstract
In today’s world where the number of images is huge and people cannot quickly retrieve the information they need, we urgently need a simpler and more human-friendly way of understanding images, and image captions have emerged. Image caption, as its name suggests, is to analyze and understand image information to generate natural language descriptions of specific images. In recent years, it has been widely used in image-text crossover studies, early infant education, and assisted by disadvantaged groups. And the favor of industry, has produced many excellent research results. At present, the evaluation of image caption is basically based on objective evaluation indicators such as BLUE and CIDEr. It is easy to prevent the generated caption from approaching human language expression. The introduction of GAN idea allows us to use a new method of adversarial training. To evaluate the generated caption, the evaluation module is more natural and comprehensive. Considering the requirements for image fidelity, this topic proposes a GAN-based image description. The Attention mechanism is introduced to improve image fidelity, which makes the generated caption more accurate and more close to human language expression.
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References
Shi, Z.: Mind Computation. World Scientific Publishing, Singapore (2017)
Vinyals, O., et al.: Show and tell: a neural image caption generator. In: Computer Vision and Pattern Recognition, pp. 3156–3164. IEEE (2015)
Mao, J., Xu, W., Yang, Y., et al.: Explain images with multimodal recurrent neural networks. arXiv preprint arXiv:1410.1090 (2014)
Vinyals, O., Toshev, A., Bengio, S., et al.: Show and tell: lessons learned from the 2015 MSCOCO image captioning challenge. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 652–663 (2016)
Hollink, L., Little, S., Hunter, J.: Evaluating the application of semantic inferencing rules to image annotation. In: International Conference on Knowledge Capture, pp. 91–98. ACM (2005)
Lu, J., Xiong, C., Parikh, D., et al.: Knowing when to look: adaptive attention via a visual sentinel for image captioning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 375–383 (2017)
Anderson, P., He, X., Buehler, C., et al.: Bottom-up and top-down attention for image captioning and visual question answering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6077–6086 (2018)
Chen, C., Mu, S., Xiao, W., et al.: Improving image captioning with conditional generative adversarial nets. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 8142–8150 (2019)
Ren, S., He, K., Girshick, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Girshick, R.: Fast R-CNN. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1440–1448. IEEE (2015)
Jia, X., et al.: Guiding the long-short term memory model for image caption generation. In: IEEE International Conference on Computer Vision, pp. 2407–2415. IEEE (2016)
Yan, S., Xie, Y., Wu, F., et al.: Image captioning via hierarchical attention mechanism and policy gradient optimization. Sig. Process. 167, 107329 (2020)
Yu, L., Zhang, W., Wang, J., et al.: SeqGAN: sequence generative adversarial nets with policy gradient. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)
Dai, B., Fidler, S., Urtasun R., et al.: Towards diverse and natural image descriptions via a conditional GAN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2970–2979 (2017)
Papineni, K., Roukos, S., Ward, T., et al.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 311–318. Association for Computational Linguistics (2002)
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Huang, Z., Shi, Z. (2020). Image Caption Combined with GAN Training Method. In: Shi, Z., Vadera, S., Chang, E. (eds) Intelligent Information Processing X. IIP 2020. IFIP Advances in Information and Communication Technology, vol 581. Springer, Cham. https://doi.org/10.1007/978-3-030-46931-3_29
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DOI: https://doi.org/10.1007/978-3-030-46931-3_29
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