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Fusing Pixels and Prose: The Transformative Impact of Integrating Computer Vision and Natural Language Processing on Multimedia Robotics Applications
Issue Vol. 3 No. 01 (2026): Volume 03 Issue 01 --- Section Articles
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Abstract
Imagine a robot that doesn't just see the world, but can tell you about it. A machine that can follow your spoken instructions not just by rote, but with a genuine understanding of the objects and actions involved. This is the future being built at the intersection of two of artificial intelligence's most powerful fields: Computer Vision (CV) and Natural Language Processing (NLP). This article explores that exciting frontier. We'll journey through the core technologies that allow machines to see and to speak, from digital "eyes" that recognize objects to "minds" that can process language. Our main focus will be on the clever methods researchers use to weave these two abilities together, creating a shared understanding between pixels and prose. We'll look at the incredible results of this fusion: robots that can narrate a video, answer questions about a photograph, guide the visually impaired, or even learn a new task simply by watching and listening. Finally, we'll have an honest conversation about the tough puzzles that still need solving—like teaching machines true commonsense—and look ahead to a future where intelligent, collaborative robots become a beneficial part of our everyday lives.
Keywords
References
[1] G. Yin, Intelligent framework for social robots based on artificial intelligence-driven mobile edge computing, Computers & Electrical Engineering, 96, Part B, (2021).
[2] Fisher, M., Cardoso, R. C., Collins, E. C., Dadswell, C., Dennis, L. A., Dixon, C., ... & Webster, M., An overview of verification and validation challenges for inspection robots, Robotics, 10, 67 (2021).
[3] A. Jamshed and M. M. Fraz, NLP Meets Vision for Visual Interpretation - A Retrospective Insight and Future directions, 2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2), 1-8 (2021).
[4] W. Fang, P. E.D. Love, H. Luo, L. Ding, Computer vision for behaviour-based safety in construction: A review and future directions, Advanced Engineering Informatics, 43, (2020).
[5] H. Sharma, Improving Natural Language Processing tasks by Using Machine Learning Techniques, 2021 5th International Conference on Information Systems and Computer Networks (ISCON), 1-5 (2021).
[6] M. Jitendra, P. Arbeláez, J. Carreira, K. Fragkiadaki, R. Girshick, G. Gkioxari, S. Gupta, B. Hariharan, A. Kar, and S. Tulsiani, The three R’s of computer vision: Recognition, reconstruction and reorganization, Pattern Recognition Letters, 72, 4-14 (2016).
[7] P. Gärdenfors, The Geometry of Meaning: Semantics Based on Conceptual Spaces, MIT Press, (2014).
[8] E. Dockrell, D. Messer, R. George, and A. Ralli, Beyond naming patterns in children with WFDs—Definitions for nouns and verbs, Journal of Neurolinguistics, 16, 191-211 (2003).
[9] A. Torfi, R. A. Shirvani, Y. Keneshloo, N. Tavaf, and E. A. Fox, Natural language processing advancements by deep learning: A survey, arXiv preprint arXiv:2003.01200 (2020).
[10] W. Graterol, J. Diaz-Amado, Y. Cardinale, I. Dongo, E. Lopes-Silva, and C. Santos-Libarino, Emotion detection for social robots based on nlp transformers and an emotion ontology, Sensors, 21, 1322 (2021).
[11] S., Zhenfeng, W. Wu, Z. Wang, W. Du, and C. Li, Seaships: A large-scale precisely annotated dataset for ship detection, IEEE transactions on multimedia, 20, 2593-2604 (2018).
[12] https://monkeylearn.com/blog/natural-language-processing-challenges/ , last vist 1/2/2022.
[13] C. Zhang, Z. Yang, X. He and L. Deng, Multimodal Intelligence: Representation Learning, Information Fusion, and Applications, in IEEE Journal of Selected Topics in Signal Processing, 14, 478-493 (2020).
[14] S. Tellex, T. Kollar, S. Dickerson, M. R. Walter, A. G. Banerjee, S. J. Teller, and N. Roy, Understanding natural language commands for robotic navigation and mobile manipulation. In Proceedings of the AAAI Conference on Artificial Intelligence, 25, 1507-1514 (2011).
[15] Y. Yezhou, C. Teo, H. Daumé III, and Y. Aloimonos, Corpus-guided sentence generation of natural images, In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, 444-454 (2011).
[16] T. S. Motwani, R. J. Mooney, Improving Video Activity Recognition using Object Recognition and Text Mining, In Proceedings of the 20th European Conference on Artificial Intelligence (ECAI-2012), 600-605 (2012).
[17] N. Krishnamoorthy, G. Malkarnenkar, R. J. Mooney, K. Saenko and S. Guadarrama, Generating Natural-Language Video Descriptions Using Text-Mined Knowledge, In Proceedings of the 27th AAAI Conference on Artificial Intelligence (AAAI-2013), 541-547 (2013).
[18] J. Thomason, S. Venugopalan, S. Guadarrama, K. Saenko, and R Mooney, Integrating language and vision to generate natural language descriptions of videos in the wild, Proceedings of the 25th International Conference on Computational Linguistics (COLING), (2014).
[19] Y. Yezhou, C. L. Teo, C. Fermüller, and Y. Aloimonos, Robots with language: Multi-label visual recognition using NLP, In IEEE International Conference on Robotics and Automation, 4256-4262 (2013).
[20] S. Aditya, Y. Yang, C. Baral, C. Fermuller, and Y. Aloimonos, From images to sentences through scene description graphs using commonsense reasoning and knowledge, arXiv preprint arXiv, (2015).
[21] R. Bernardi, R. Cakici, D. Elliott, A. Erdem, E. Erdem, N. I. Cinbis, F. Keller, A. Muscat, and B. Plank, Automatic description generation from images: A survey of models, datasets, and evaluation measures, Journal of Artificial Intelligence Research, 55, 409-442 (2016).
[22] P. Das, C. Xu, R. Doell, and J. Corso, A thousand frames in just a few words: Lingual description of videos through latent topics and sparse object stitching, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2634-264 (2013).
[23] Y. Gong, Q. Ke, M. Isard, and S. Lazebnik, A multi-view embedding space for modeling internet images, tags, and their semantics, International journal of computer vision, 106, 210-233 (2014).
[24] A. Karpathy and L. Fei-Fei, Deep visual-semantic alignments for generating image descriptions, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3128-3137 (2015).
[25] R. Schwartz, R. Reichart and A. Rappoport, Symmetric pattern based word embeddings for improved word similarity prediction, In CoNLL, 2015, 258-267 (2015).
[26] N. Shukla, C. Xiong, and S. C. Zhu, A unified framework for human-robot knowledge transfer, In Proceedings of the 2015 AAAI Fall Symposium Series, (2015).
[27] Carina Silberer, Vittorio Ferrari, and Mirella Lapat, Models of semantic representation with visual attributes, In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, 572-582 (2013).
[28] R. Socher, A. Karpathy, Q. V. Le, C. D. Manning, and A. Y. Ng, Grounded compositional semantics for finding and describing images with sentences. Transactions of the Association for Computational Linguistics, 2, 207-218 (2014).
[29] M. Tapaswi, M. B¨auml, and R. Stiefelhagen, Book2movie: Aligning video scenes with book chapters. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1827–1835 (2015).
[30] I. Abdalla Mohamed, A. Ben Aissa, L. F. Hussein, Ahmed I. Taloba, and T. kallel, A new model for epidemic prediction: COVID-19 in kingdom saudi arabia case study”, Materials Today: Proceedings, (2021).
[31] Ahmed. I. Taloba and S. S. I. Ismail, An Intelligent Hybrid Technique of Decision Tree and Genetic Algorithm for E-Mail Spam Detection, Ninth International Conference on Intelligent Computing and Information Systems (ICICIS), 99-104 (2019).
[32] Ahmed I. Taloba, M. R. Riad and T. H. A. Soliman, Developing an efficient spectral clustering algorithm on large scale graphs in spark, Eighth International Conference on Intelligent Computing and Information Systems (ICICIS), 292-298 (2017).
[33] D. Tang, B. Qin, and T. Liu, “Document modeling with gated recurrent neural network for sentiment classification,” in Proceedings of the 2015 conference on empirical methods in natural language processing, 2015, pp. 1422–1432.
[34] Q. Le and T. Mikolov, “Distributed representations of sentences and documents,” in International conference on machine learning, 2014, pp. 1188–1196.
[35] S. R. Bowman, G. Angeli, C. Potts, and C. D. Manning, “A large annotated corpus for learning natural language inference,” ArXiv Prepr. ArXiv150805326, 2015.
[36] Y. Yang, W. Yih, and C. Meek, “Wikiqa: A challenge dataset for open-domain question answering,” in Proceedings of the 2015 conference on empirical methods in natural language processing, 2015, pp. 2013–2018.
[37] W. Yih, M. Richardson, C. Meek, M.-W. Chang, and J. Suh, “The value of semantic parse labeling for knowledge base question answering,” in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2016, pp. 201–206.
[38] A. Fukui, D. H. Park, D. Yang, A. Rohrbach, T. Darrell, and M. Rohrbach, “Multimodal compact bilinear pooling for visual question answering and visual grounding,” ArXiv Prepr. ArXiv160601847, 2016.
[39] N. Kalchbrenner, E. Grefenstette, and P. Blunsom, “A convolutional neural network for modelling sentences,” ArXiv Prepr. ArXiv14042188, 2014.
[40] T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” Adv. Neural Inf. Process. Syst., vol. 26, 2013.
