Explainable Multimodal Transformer Architecture for Context-Aware Decision Intelligence in Smart Applications
Main Article Content
Abstract
Smart applications increasingly depend on information obtained from multiple sources, including images, text, speech, sensor measurements, video streams, and contextual information. Although combining these modalities can provide a more comprehensive representation of a user's environment, conventional multimodal learning systems frequently provide limited insight into how individual information sources influence their final decisions. This lack of transparency can restrict the deployment of intelligent systems in applications where decisions need to be understood, verified, or reviewed by human users. This research proposes an explainable multimodal Transformer architecture for context-aware decision intelligence in smart applications. The proposed architecture employs separate modality encoders to transform heterogeneous inputs into compatible representations, followed by cross-modal Transformer layers that learn relationships among visual, textual, auditory, and contextual features. A context integration mechanism is introduced to dynamically adjust the contribution of each modality according to the current application state and available evidence. Instead of generating a prediction alone, the architecture produces an associated explanation identifying the principal modalities, features, and contextual elements contributing to the decision. A hierarchical explanation mechanism provides both modality-level and feature-level interpretations, allowing users to examine why a particular decision was generated and which information sources had the greatest influence. The framework is designed to handle incomplete or unevenly informative modalities by dynamically modifying their contribution during inference. Its performance can be evaluated using predictive accuracy together with explanation fidelity, modality contribution consistency, robustness, context sensitivity, and computational efficiency. The proposed architecture provides a unified approach for combining multimodal perception with interpretable decision intelligence and can support smart healthcare, intelligent transportation, smart homes, human–computer interaction, industrial monitoring, and context-aware assistance. The study establishes a framework in which multimodal Transformer reasoning is accompanied by evidence-oriented explanations suitable for human inspection.
Article Details
Section
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
How to Cite
References
[1]. A. Vaswani et al., “Attention Is All You Need,” Advances in Neural Information Processing Systems, vol. 30, 2017.
[2]. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” Proceedings of NAACL-HLT, pp. 4171–4186, 2019.
[3]. J. Lu, D. Batra, D. Parikh, and S. Lee, “ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks,” Advances in Neural Information Processing Systems, vol. 32, 2019. ViLBERT uses separate visual and textual streams connected through co-attentional Transformer layers.
[4]. H. Tan and M. Bansal, “LXMERT: Learning Cross-Modality Encoder Representations from Transformers,” Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pp. 5100–5111, 2019. LXMERT explicitly models relationships within and across vision and language modalities.
[5]. L. H. Li, M. Yatskar, D. Yin, C.-J. Hsieh, and K.-W. Chang, “VisualBERT: A Simple and Performant Baseline for Vision and Language,” Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 1001–1015, 2020. VisualBERT employs Transformer self-attention to align textual elements with visual regions.
[6]. Y.-C. Chen, L. Li, L. Yu, A. El Kholy, F. Ahmed, Z. Gan, Y. Cheng, and J. Liu, “UNITER: UNiversal Image-TExt Representation Learning,” European Conference on Computer Vision, pp. 104–120, 2020. UNITER develops joint image-text representations through large-scale multimodal pretraining and explicit word-region alignment.
[7]. A. Radford et al., “Learning Transferable Visual Models From Natural Language Supervision,” Proceedings of the 38th International Conference on Machine Learning, vol. 139, pp. 8748–8763, 2021. CLIP demonstrated large-scale image-text representation learning and zero-shot transfer across visual tasks.
[8]. J.-B. Alayrac et al., “Flamingo: A Visual Language Model for Few-Shot Learning,” Advances in Neural Information Processing Systems, vol. 35, pp. 23716–23736, 2022.
[9]. P. Xu, X. Zhu, and D. A. Clifton, “Multimodal Learning With Transformers: A Survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 10, pp. 12113–12132, 2023. This survey reviews multimodal Transformer architectures, multimodal pretraining, applications, challenges, and open research directions.
[10]. D. H. Park, L. A. Hendricks, Z. Akata, A. Rohrbach, B. Schiele, T. Darrell, and M. Rohrbach, “Multimodal Explanations: Justifying Decisions and Pointing to the Evidence,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8779–8788, 2018. The study combines visual evidence with textual rationales to explain multimodal decisions.
[11]. S. Lundberg and S.-I. Lee, “A Unified Approach to Interpreting Model Predictions,” Advances in Neural Information Processing Systems, vol. 30, 2017. The paper introduced SHAP as a unified framework for attributing model predictions to input features.
[12]. M. B K, V. K. Bharadwaj Parasaram, S. K. Nalluri and V. T. Bathini, "Attention-Guided Multi-Modal Deep Intelligence Framework for Early Cancer Detection Using Imaging and Genomic Signatures," 2026 International Conference on Data Science for Cyber-Physical Systems Resilience using Advanced Applications (ICDCA), BANGALORE, India, 2026, pp. 1-6, doi: 10.1109/ICDCA69396.2026.11620255.
[13]. M. Sundararajan, A. Taly, and Q. Yan, “Axiomatic Attribution for Deep Networks,” Proceedings of the 34th International Conference on Machine Learning, pp. 3319–3328, 2017. The work introduced Integrated Gradients for attribution of predictions to input features.
[14]. R. R. Selvaraju et al., “Grad-CAM: Visual Explanations From Deep Networks via Gradient-Based Localization,” Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626, 2017.
[15]. L. H. Gilpin et al., “Explaining Explanations: An Overview of Interpretability of Machine Learning,” 2018 IEEE 5th International Conference on Data Science and Advanced Analytics, pp. 80–89, 2018.
[16]. M. T. Ribeiro, S. Singh, and C. Guestrin, “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, pp. 1135–1144, 2016.
[17]. L. Parcalabescu and A. Frank, “MM-SHAP: A Performance-Agnostic Metric for Measuring Multimodal Contributions in Vision and Language Models & Tasks,” Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, pp. 4032–4059, 2023. MM-SHAP specifically addresses the contribution of different modalities in vision-language models, making it particularly relevant to the proposed architecture.
[18]. Koneni, C., S. Lokula, and L. Panjiyar. "A Cloud-Native Approach to Statistical Anomaly Detection and Automated Data Quality Validation." International Journal of Data Science and IoT Management System 4.4 (2025): 533-543
[19]. P. Fantozzi and M. Naldi, “The Explainability of Transformers: Current Status and Directions,” Computers, vol. 13, no. 4, Art. 92, 2024. The survey categorizes Transformer explainability methods according to the Transformer components used to produce explanations.
[20]. S. Sun, W. An, F. Tian, F. Nan, Q. Liu, J. Liu, N. Shah, and P. Chen, “A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future,” 2024. The review examines multimodal XAI approaches, evaluation methods, datasets, and emerging challenges across different generations of multimodal models.
[21]. V. K. Bharadwaj Parasaram, V. Teja Bathini and S. K. Nalluri, "An Intelligent Digital Framework for Clinical Trial Monitoring Using AI and Cloud-Based Software Platforms," 2026 2nd International Conference on Artificial Intelligence Systems (AIS), San Antonio, TX, USA, 2026, pp. 524-530, doi: 10.1109/AIS69919.2026.11621841.
[22]. Vishram Singh, 2026. "Enterprise Resource Planning for Financial Applications: Engineering, Architecture, and Design Principles," International Journal of Scientific Research in Science and Technology, Technoscience Academy, vol. 13(2), pages 653-661, April.
[23]. C. Koneni and S. Lokula, "Silent Data Corruption in Distributed Genai-Augmented Data Pipelines: an Empirical Study of Hallucination-Induced Failure Propagation," 2026 IEEE 46th International Conference on Distributed Computing Systems Workshops (ICDCSW), Seoul, Korea, Republic of, 2026, pp. 137-143,