  Image classification models often demonstrate unstable performance in
real-world applications due to variations in image information, driven by
differing visual perspectives of subject objects and lighting discrepancies. To
mitigate these challenges, existing studies commonly incorporate additional
modal information matching the visual data to regularize the model's learning
process, enabling the extraction of high-quality visual features from complex
image regions. Specifically, in the realm of multimodal learning, cross-modal
alignment is recognized as an effective strategy, harmonizing different modal
information by learning a domain-consistent latent feature space for visual and
semantic features. However, this approach may face limitations due to the
heterogeneity between multimodal information, such as differences in feature
distribution and structure. To address this issue, we introduce a Multimodal
Alignment and Reconstruction Network (MARNet), designed to enhance the model's
resistance to visual noise. Importantly, MARNet includes a cross-modal
diffusion reconstruction module for smoothly and stably blending information
across different domains. Experiments conducted on two benchmark datasets,
Vireo-Food172 and Ingredient-101, demonstrate that MARNet effectively improves
the quality of image information extracted by the model. It is a plug-and-play
framework that can be rapidly integrated into various image classification
frameworks, boosting model performance.
