Seismic advances in generative AI algorithms for imagery, text, and other data types has led
to the temptation to use synthetic data to train next-generation models. Repeating this …

Recent advancements in Multimodal Large Language Models (MLLMs) have been utilizing
Visual Prompt Generators (VPGs) to convert visual features into tokens that LLMs can …

Generative data augmentation (GDA) has emerged as a promising technique to alleviate
data scarcity in machine learning applications. This thesis presents a comprehensive survey …

Generative data augmentation (GDA) has emerged as a promising technique to alleviate
data scarcity in machine learning applications. This thesis presents a comprehensive survey …

As AI model size grows, neural scaling laws have become a crucial tool to predict the
improvements of large models when increasing capacity and the size of original (human or …

The advancement of visual intelligence is intrinsically tethered to the availability of data. In
parallel, generative Artificial Intelligence (AI) has unlocked the potential to create synthetic …

Few-shot object detection (FSOD) aims at learning a novel class object detector with
abundant base class samples and a limited number of novel class samples. Some recent …

Recent text-to-image generation models have shown promising results in generating high-
fidelity photo-realistic images. In parallel, the problem of data scarcity has brought a growing …

While text-to-image diffusion models have been shown to achieve state-of-the-art results in
image synthesis, they have yet to prove their effectiveness in downstream applications …

Recent advances in generative deep learning have enabled the creation of high-quality
synthetic images in text-to-image generation. Prior work shows that fine-tuning a pretrained …