CrossFusionXNet: Adaptive Multimodal Knowledge Transfer Framework Utilizing Transformers and Cross-Modal Attention for Unified Representation

Recent emergence of large-scale and complex multimodal data from multiple modalities including text, image and audio calls for efficient knowledge transfer across modalities. To meet this need, a multimodal transfer learning approach has been developed to incorporate information from other modalities, to allow models to benefit from the shared representations and relationship between the modalities. This work presents the proposed CrossFusionXNet, an adaptive framework utilizing transformers and cross-modal attention to achieve unified and accurate knowledge transfer. In this paper, the existing studies are critically reviewed to reveal open issues in cross-modal knowledge transfer, such as the transfer between texts, images and Audio. The proposed algorithm introduces an adaptive learning framework that dynamically adjusts transfer parameters based on modality-specific characteristics and inter-modal relationships. In the suggested CrossFusionXNet, a three-phase transfer learning strategy is employed. First, modality-specific encoders based on transformer networks with self-attention mechanisms are constructed to obtain features of higher dimensions. Each of these encoders includes residual connections and layer normalization to keep gradients stable across transfer between modalities. Second, a new cross-modal attention module with a multihead attention mechanism with learnable temperature scaling is proposed for dynamic weighting of inter-modal relationships. The last phase implements an adaptive optimization strategy utilizing meta-learning principles to regulate the learning rate together with the transfer weights based on modality-specific loss gradients and cross-modality alignment metrics. The experimental results illustrate that the proposed approach outperforms the existing methods in cross-modal knowledge transfer to a great extent according to the results of the experimental evaluation.