1 citations · 1 across the 4 of their papers we have counts for
6 papers
Joint and Cross-Modal Video-Audio Generation and Editing: A Unified Formulation and Design Taxonomy
Abhinav Sharma, Sai Karthik Navuluru, Wang Wei +21
Video and audio are perceived together, yet most generative models treat them in isolation. We examine methods that model the two modalities jointly, generate one from the other, o…
InfinityStory: Unlimited Video Generation with World Consistency and Character-Aware Shot Transitions
Mohamed Elmoghany, Liangbing Zhao, Xiaoqian Shen +27
Generating long-form storytelling videos with consistent visual narratives remains a significant challenge in video synthesis. We present a novel framework, dataset, and a model th…
A Survey on Long-Video Storytelling Generation: Architectures, Consistency, and Cinematic Quality
Mohamed Elmoghany, Ryan Rossi, Seunghyun Yoon +26
Despite the significant progress that has been made in video generative models, existing state-of-the-art methods can only produce videos lasting 5-16 seconds, often labeled "long-…
Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey
Bo Ni, Zheyuan Liu, Leyao Wang +17
Retrieval-Augmented Generation (RAG) is an advanced technique designed to address the challenges of Artificial Intelligence-Generated Content (AIGC). By integrating context retriev…
Forward Learning of Graph Neural Networks
Namyong Park, Xing Wang, Antoine Simoulin +5
Graph neural networks (GNNs) have achieved remarkable success across a wide range of applications, such as recommendation, drug discovery, and question answering. Behind the succes…
GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection
Namyong Park, Ryan Rossi, Xing Wang +3
The choice of a graph learning (GL) model (i.e., a GL algorithm and its hyperparameter settings) has a significant impact on the performance of downstream tasks. However, selecting…