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Omni AI Cloud: An Intelligent Unified Multi-Modal AI System with Automatic Model Selection and Explainability
Authors
1 3 4 5 Artificial Intelligence and Machine Learning, Sasi Institute of Technology and Engineering, Tadepalligude, Andhra Pradesh, India. 2 Head of the Department, Artificial Intelligence and Machine Learning, Sasi Institute of Technology and Engineering, Tadepalligudem, Andhra Pradesh, India.
Article Information
- Organised By
- Madanapalle Institute of Technology & Science (MITS)
- Conference Date
- 9 & 10 March 2026
- Pages
- 36-43
- Article Type
- Research Article
- Check for updates
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References
1. He, K., Zhang, X., Ren, S., & Sun, J. Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778.
2. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. Attention is all you need. Advances in Neural Information Processing Systems (NeurIPS), 2017, pp. 5998–6008.
3. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT), 2019, pp. 4171–4186.
4. Ribeiro, M. T., Singh, S., & Guestrin, C. Why should I trust you? Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 1135–1144.
5. Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. Microsoft COCO: Common objects in context. European Conference on Computer Vision (ECCV), 2014, pp. 740–755.
6. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 779–788.
7. Li, J., Li, D., Xiong, C., & Hoi, S. C. H. BLIP:Bootstrapping language-image pre-training for unified vision-language understanding and generation. Proceedings of the International Conference on Machine Learning (ICML), 2022.
8. Sanh, V., Debut, L., Chaumond, J., & Wolf, T. DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter. Proceedings of the 5th Workshop on Energy Efficient Machine Learning and Cognitive Computing (EMNLP Workshop), 2019.
9. Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140), 1–67, 2020.
10. Du, Y., Li, C., Guo, R., Yin, X., Liu, W., Zhou, J., Bai,Y., Yu, Z., Yang, Y., & Dang, Q. PP-OCR: A practical ultra lightweight OCR system. arXiv preprint arXiv:2009.09941, 2020.
11. Singh, R., Kaur, M., & Sharma, A. Automated document processing combining OCR and generative artificial intelligence for efficient extraction and summarization. International Journal of Intelligent Systems, 2023.
12. Kim, G., Hong, T., Yim, M., Nam, J., Park, J., Yim, J.,& Ha, J.-W. Donut: Document understanding transformer without OCR. European Conference on Computer Vision (ECCV), 2022.
13. Sharma, R., Gupta, A., & Verma, S. Developing a unified multimodal framework for document intelligence and adaptive process automation in smart organizations. Journal of Intelligent Information Systems, 2023.
14. Li, Y., Chen, Z., Yu, J., & Zhang, S. DocAgent: An agentic framework for multi-modal long-context document understanding. Proceedings of the AAAI Conference on Artificial Intelligence, 2024.
15. Costa-jussà, M. R., Cross, J., Çelebi, O., Elbayad, M.,Heafield, K., He, T., Li, Y., Sun, S., Tang, J., Tran, C., Ye, S., Zettlemoyer, L., & Barrault, L. Scaling neural machine translation to 200 languages. Nature, 2022, 610, 439–446.
2. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. Attention is all you need. Advances in Neural Information Processing Systems (NeurIPS), 2017, pp. 5998–6008.
3. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT), 2019, pp. 4171–4186.
4. Ribeiro, M. T., Singh, S., & Guestrin, C. Why should I trust you? Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 1135–1144.
5. Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. Microsoft COCO: Common objects in context. European Conference on Computer Vision (ECCV), 2014, pp. 740–755.
6. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 779–788.
7. Li, J., Li, D., Xiong, C., & Hoi, S. C. H. BLIP:Bootstrapping language-image pre-training for unified vision-language understanding and generation. Proceedings of the International Conference on Machine Learning (ICML), 2022.
8. Sanh, V., Debut, L., Chaumond, J., & Wolf, T. DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter. Proceedings of the 5th Workshop on Energy Efficient Machine Learning and Cognitive Computing (EMNLP Workshop), 2019.
9. Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140), 1–67, 2020.
10. Du, Y., Li, C., Guo, R., Yin, X., Liu, W., Zhou, J., Bai,Y., Yu, Z., Yang, Y., & Dang, Q. PP-OCR: A practical ultra lightweight OCR system. arXiv preprint arXiv:2009.09941, 2020.
11. Singh, R., Kaur, M., & Sharma, A. Automated document processing combining OCR and generative artificial intelligence for efficient extraction and summarization. International Journal of Intelligent Systems, 2023.
12. Kim, G., Hong, T., Yim, M., Nam, J., Park, J., Yim, J.,& Ha, J.-W. Donut: Document understanding transformer without OCR. European Conference on Computer Vision (ECCV), 2022.
13. Sharma, R., Gupta, A., & Verma, S. Developing a unified multimodal framework for document intelligence and adaptive process automation in smart organizations. Journal of Intelligent Information Systems, 2023.
14. Li, Y., Chen, Z., Yu, J., & Zhang, S. DocAgent: An agentic framework for multi-modal long-context document understanding. Proceedings of the AAAI Conference on Artificial Intelligence, 2024.
15. Costa-jussà, M. R., Cross, J., Çelebi, O., Elbayad, M.,Heafield, K., He, T., Li, Y., Sun, S., Tang, J., Tran, C., Ye, S., Zettlemoyer, L., & Barrault, L. Scaling neural machine translation to 200 languages. Nature, 2022, 610, 439–446.
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