The field of multimodal learning is moving towards more sophisticated and nuanced approaches to image-text matching, with a focus on addressing the challenges of ambiguity, high-order associations, and semantic uncertainties. Recent developments have introduced innovative frameworks that leverage dynamic clustering, adaptive aggregation, and momentum contrastive learning to enhance the accuracy and efficiency of image-text matching. Another key direction is the integration of knowledge augmentation, emotion guidance, and balanced learning to improve the detection of multimodal fake news. Moreover, there is a growing interest in composed image retrieval, with novel methods proposing multi-faceted chain-of-thought, re-ranking, and multi-stage fusion approaches to facilitate precise retrieval. Notable papers in this regard include: AAHR, which proposes a unified representation space to mitigate the soft positive sample problem and introduces global and local feature extraction mechanisms to enhance full-grained semantic understanding. QuRe, which optimizes a reward model objective to reduce false negatives and introduces a hard negative sampling strategy to effectively filter false negatives. AdaptiSent, which uses adaptive cross-modal attention mechanisms to improve sentiment classification and aspect term extraction from both text and images. MCoT-RE, which utilizes multi-faceted Chain-of-Thought to guide the multimodal large language model to balance explicit modifications and contextual visual cues. FAR-Net, which proposes a multi-stage fusion framework with enhanced semantic alignment and adaptive reconciliation to integrate two complementary modules.