5 papers
Joint Enhancement and Classification using Coupled Diffusion Models of Signals and Logits
Gilad Nurko, Roi Benita, Yehoshua Dissen +4
Robust classification in noisy environments remains a fundamental challenge in machine learning. Standard approaches typically treat signal enhancement and classification as separa…
Analyzing and Guiding Zero-Shot Posterior Sampling in Diffusion Models
Roi Benita, Michael Elad, Joseph Keshet
Recovering a signal from its degraded measurements is a long standing challenge in science and engineering. Recently, zero-shot diffusion based methods have been proposed for such…
LTX-2: Efficient Joint Audio-Visual Foundation Model
Yoav HaCohen, Benny Brazowski, Nisan Chiprut +26
Recent text-to-video diffusion models can generate compelling video sequences, yet they remain silent -- missing the semantic, emotional, and atmospheric cues that audio provides.…
Spectral Analysis of Diffusion Models with Application to Schedule Design
Roi Benita, Michael Elad, Joseph Keshet
Diffusion models (DMs) have emerged as powerful tools for modeling complex data distributions and generating realistic new samples. Over the years, advanced architectures and sampl…
CAFA: a Controllable Automatic Foley Artist
Roi Benita, Michael Finkelson, Tavi Halperin +2
Foley is a key element in video production, refers to the process of adding an audio signal to a silent video while ensuring semantic and temporal alignment. In recent years, the r…