In the rapidly advancing realm of visual generation, diffusion models have revolutionized the
landscape, marking a significant shift in capabilities with their impressive text-guided …

As one of the most popular parameter-efficient fine-tuning (PEFT) methods, low-rank
adaptation (LoRA) is commonly applied to fine-tune large language models (LLMs) …

Abstract Text-to-image (T2I) generative models have attracted significant attention and found
extensive applications within and beyond academic research. For example the Civitai …

Large Language Models (LLMs) have showcased exceptional performance across a wide
array of Natural Language Processing (NLP) tasks. Fine-tuning techniques are commonly …

We explore the problem of computationally generating specialprime'images that produce
optical illusions when physically arranged and viewed in a certain way. First, we propose a …

Although motivated by the adaptation of text-to-speech synthesis models, we argue that
more generic parameter-efficient fine-tuning (PEFT) is an appropriate framework to do such …

Few-shot object detection (FSOD) in remote sensing images (RSIs) aims to achieve object
location and classification with only few training samples. Currently, mainstream transfer …

Recent advancements in generative models have significantly impacted content creation,
leading to the emergence of Personalized Content Synthesis (PCS). With a small set of user …

Low-rank adaptation (LoRA) has become the standard approach for parameter-efficient fine-
tuning of large language models (LLM), but our theoretical understanding of LoRA has been …

Low-rank adaptation (LoRA) and its variants are widely employed in fine-tuning large
models, including large language models for natural language processing and diffusion …