Image Text Alignment

Image-text alignment focuses on improving the correspondence between visual and textual representations, aiming to create models that accurately understand and generate images based on textual descriptions, or vice-versa. Current research emphasizes enhancing the alignment within various model architectures, including diffusion transformers and vision-language models, often through techniques like contrastive learning, attention modulation, and fine-tuning strategies that leverage large language models or image-to-text concept matching. This work is crucial for advancing applications such as text-to-image generation, image captioning, and weakly supervised semantic segmentation, ultimately leading to more robust and interpretable multimodal AI systems.