A Cross-Modal Alignment Method Based on Adaptive Feature Aggregation and Spatial Fusion Mechanism

Multimodal tasks, such as vision-language integration, require efficient and adaptive frameworks to address challenges like feature imbalance, high computational cost, and cross-modal representation alignment. In this paper, we propose a novel framework that combines a lightweight cross-modal Transformer with an adaptive feature aggregation strategy to enhance multimodal feature integration capabilities. The proposed method includes a cross-modal adapter for feature alignment, an adaptive feature aggregation module to balance modality-specific feature representations, and a spatial adaptive fusion mechanism for optimized information extraction. Experiments on benchmark datasets demonstrate that the proposed framework achieves competitive performance in both zero-shot and fine-tuning scenarios for vision-language tasks. This study highlights the potential of adaptive cross-modal approaches in scalable, robust, and computationally efficient multimodal applications.