6 papers
Event-Driven Video Generation
Chika Maduabuchi, Jindong Wang
Current text-to-video models can make individual frames look convincing while still getting simple interactions wrong: objects move before contact, an intended action is skipped, a…
Entropy-Controlled Flow Matching
Chika Maduabuchi
Modern vision generators transport a base distribution to data through time-indexed measures, implemented as deterministic flows (ODEs) or stochastic diffusions (SDEs). Despite str…
Corruption-Aware Training of Latent Video Diffusion Models for Robust Text-to-Video Generation
Chika Maduabuchi, Hao Chen, Yujin Han +1
Latent Video Diffusion Models (LVDMs) have achieved state-of-the-art generative quality for image and video generation; however, they remain brittle under noisy conditioning, where…
MSEG-VCUQ: Multimodal SEGmentation with Enhanced Vision Foundation Models, Convolutional Neural Networks, and Uncertainty Quantification for High-Speed Video Phase Detection Data
Chika Maduabuchi, Ericmoore Jossou, Matteo Bucci
High-speed video (HSV) phase detection (PD) segmentation is crucial for monitoring vapor, liquid, and microlayer phases in industrial processes. While CNN-based models like U-Net h…
Temporal Pair Consistency for Variance-Reduced Flow Matching
Chika Maduabuchi, Jindong Wang
Continuous-time generative models, such as diffusion models, flow matching, and rectified flow, learn time-dependent vector fields but are typically trained with objectives that tr…
VideoSAM: A Large Vision Foundation Model for High-Speed Video Segmentation
Chika Maduabuchi, Ericmoore Jossou, Matteo Bucci
High-speed video (HSV) segmentation is essential for analyzing dynamic physical processes in scientific and industrial applications, such as boiling heat transfer. Existing models…