4 accepted papers
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating external knowledge, where the LLM
Retrieval-augmented generation (RAG) has become a fundamental paradigm for addressing the challenges faced by large language models in handling real-time information and domain-specific problems. Traditional RAG systems primarily rely on the in-context learning (ICL) capabilities of the large langua
Few-shot Font Generation (FFG) aims to create new font libraries using limited reference glyphs, with crucial applications in digital accessibility and equity for low-resource languages, especially in multilingual artificial intelligence systems. Although existing methods have shown promising perfor…
Transformer-based methods have demonstrated impressive achievements in the realm of single image deraining (SID). Nonetheless, these advancements still confront a significant challenge: the employed self-attention mechanisms facilitate homogeneous feature aggregation, consequently restricting the ca…