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With the rapid progression of deep learning technologies, multi-modalityimage fusion has become increasingly prevalent in object detection tasks.Despite its popularity, the inherent disparities in how different sourcesdepict scene content make fusion a challenging problem. Current fusionmethodologies identify shared characteristics between the two modalities andintegrate them within this shared domain using either iterative optimization ordeep learning architectures, which often neglect the intricate semanticrelationships between modalities, resulting in a superficial understanding ofinter-modal connections and, consequently, suboptimal fusion outcomes. Toaddress this, we introduce a text-guided multi-modality image fusion methodthat leverages the high-level semantics from textual descriptions to integratesemantics from infrared and visible images. This method capitalizes on thecomplementary characteristics of diverse modalities, bolstering both theaccuracy and robustness of object detection. The codebook is utilized toenhance a streamlined and concise depiction of the fused intra- andinter-domain dynamics, fine-tuned for optimal performance in detection tasks.We present a bilevel optimization strategy that establishes a nexus between thejoint problem of fusion and detection, optimizing both processes concurrently.Furthermore, we introduce the first dataset of paired infrared and visibleimages accompanied by text prompts, paving the way for future research.Extensive experiments on several datasets demonstrate that our method not onlyproduces visually superior fusion results but also achieves a higher detectionmAP over existing methods, achieving state-of-the-art results.

From Text to Pixels: A Context-Aware Semantic Synergy Solution for Infrared and Visible Image Fusion