3 papers
cs.CV2025
Comparing Learning Paradigms for Egocentric Video Summarization
Daniel Wen
In this study, we investigate various computer vision paradigms - supervised learning, unsupervised learning, and prompt fine-tuning - by assessing their ability to understand and…
eess.IV2024
Improving Generative Adversarial Networks for Video Super-Resolution
Daniel Wen
In this research, we explore different ways to improve generative adversarial networks for video super-resolution tasks from a base single image super-resolution GAN model. Our pri…
cs.CV2024
Directed Domain Fine-Tuning: Tailoring Separate Modalities for Specific Training Tasks
Daniel Wen, Nafisa Hussain
Large language models (LLMs) and large visual language models (LVLMs) have been at the forefront of the artificial intelligence field, particularly for tasks like text generation,…