#AI-Generated Content

Retrieval-Augmented Generation (RAG) is enhancing AI-generated content by integrating retrieval methods with generative AI. This comprehensive survey categorizes research on RAG's foundations and methods, including query-based, latent representation-based, logit-based, and speculative approaches. It highlights advancements in input, retrieval, and generation enhancements and covers applications like open-domain question answering and video captioning. Keep updated with this extensive review on how RAG influences AI content development.

A well-organized resource featuring a comprehensive selection of seminal 3D generative papers, benchmarks, datasets, and implementations. It examines critical advancements in 3D AI content, focusing on methodologies such as text-to-3D generation and neural radiance fields. This collection underscores major contributions to the field, covering applications like scene synthesis and human avatar creation. Researchers and practitioners can engage with novel developments to enhance their understanding and application of these techniques. This platform is perfect for those looking to stay informed about emerging trends in 3D AI content creation.

This survey explores Retrieval-Augmented Generation (RAG), covering diverse techniques like query and data augmentation, with ongoing updates reflecting the field's growth. It offers insights into advanced applications in content generation and knowledge base enhancement.