Semantic Transfer

Semantic transfer focuses on leveraging knowledge from one domain (e.g., text descriptions, images with different label granularities) to improve performance in another related domain, such as image segmentation, 3D attribute editing, or event coreference resolution. Current research explores various approaches, including generative adversarial networks (GANs), transformer-based models, and linear mappings between different modalities, often incorporating pre-trained models like CLIP to enhance semantic understanding. This research is significant because it addresses the challenges of limited labeled data in many computer vision and natural language processing tasks, leading to more efficient and robust models with broader applicability.