Multimodal Generation Capability

Multimodal generation focuses on creating models capable of generating diverse outputs (e.g., images, text) from various input modalities, aiming for seamless integration and coherent results. Current research emphasizes developing large, open-source models employing autoregressive architectures and innovative training strategies like efficient fine-tuning and multi-task learning with diverse data sources (including image features, metadata, and structured data). These advancements enable more controllable and fine-grained generation, improving the quality and expanding the scope of applications in areas such as image captioning, visual question answering, and creative content generation. The development of robust evaluation datasets and methods for assessing accountability and rejection of inappropriate instructions are also key areas of focus.