4 papers
Preparation Meets Opportunity: Enhancing Data Preprocessing for ML Training With Seneca
Omkar Desai, Ziyang Jiao, Shuyi Pei +2
Input data preprocessing is a common bottleneck when concurrently training multimedia machine learning (ML) models in modern systems. To alleviate these bottlenecks and reduce the…
Extreme Blind Image Restoration via Prompt-Conditioned Information Bottleneck
Hongeun Kim, Bryan Sangwoo Kim, Jong Chul Ye
Blind Image Restoration (BIR) methods have achieved remarkable success but falter when faced with Extreme Blind Image Restoration (EBIR), where inputs suffer from severe, compounde…
FlowDPS: Flow-Driven Posterior Sampling for Inverse Problems
Jeongsol Kim, Bryan Sangwoo Kim, Jong Chul Ye
Flow matching is a recent state-of-the-art framework for generative modeling based on ordinary differential equations (ODEs). While closely related to diffusion models, it provides…
FreeGuide: Training-Free Text-to-Video Alignment using Image LVLM
Jaemin Kim, Bryan Sangwoo Kim, Jong Chul Ye
Diffusion models have achieved impressive results in generative tasks for text-to-video (T2V) synthesis. However, achieving accurate text alignment in T2V generation remains challe…