MKNNet: Knowledge-aligned multimodal transformer for information retrieval - Xiaoqin Lin; Chentao Han; Jian Yao; Yue Li; Xujun Wang; Shufeng Jia (Alexandria Engineering Journal)
With the rapid advancement of artificial intelligence and the Internet of Things, data collected from multiple sensing modalities is growing rapidly in both volume and complexity. In this paper, we propose a novel deep learning framework called MKNNet, which combines modality alignment, Transformer-based fusion, and multi-loss optimization to construct a unified semantic embedding space for multimodal information retrieval. Our model leverages modality-specific encoders and attention-based fusion to achieve deep semantic consistency across modalities. Experimental results on MS-COCO and Flickr30K datasets demonstrate that MKNNet significantly outperforms state-of-the-art models such as CLIP and BLIP in terms of Recall and mAP. The proposed method enhances semantic alignment and retrieval accuracy, showing great potential for applications in smart cities, healthcare, and other multimodal Internet of Things scenarios.