Image Text Matching

Image-text matching aims to computationally measure the semantic similarity between images and their corresponding textual descriptions, enabling tasks like image retrieval and visual question answering. Current research emphasizes improving the robustness and efficiency of matching models by addressing dataset biases, incorporating richer contextual information (e.g., object relations, scene graphs), and developing more sophisticated alignment mechanisms (e.g., multi-view attention, graph-based methods). These advancements are crucial for applications ranging from large-scale multimedia search to enhancing educational materials and detecting online misinformation, driving progress in both computer vision and natural language processing.