2 citations · 4 across the 7 of their papers we have counts for
9 papers
SepGen: Multi-Stem Audio-Video Separation and Generation in a Single Model
Aviad Dahan, Rajaei Khatib, Yonatan Bitton +3
A 4D audio-visual scene comprises a video, the dynamic geometry it depicts, and the sound sources that populate it, each with its own position and trajectory. Rendering such a scen…
The Attention Triangle in Audio-Video Models
Sagi Polaczek, Noa Kraicer, Gal Metzer +4
Audio-video diffusion models rely on cross-modal attention to coordinate text, sound, and visual content, yet this same mechanism can introduce subtle and systematic semantic leaka…
Do multimodal models imagine electric sheep?
Santhosh Kumar Ramakrishnan, Carl Vondrick, Raja Giryes +2
Yes. We find that large multimodal models develop mental imagery when solving spatial puzzles, and they do imagine sheep when solving sheep puzzles. We fine-tune a Qwen3.5 VLM to s…
Video Analysis and Generation via a Semantic Progress Function
Gal Metzer, Sagi Polaczek, Ali Mahdavi-Amiri +2
Transformations produced by image and video generation models often evolve in a highly non-linear manner: long stretches where the content barely changes are followed by sudden, ab…
Anatomical Token Uncertainty for Transformer-Guided Active MRI Acquisition
Lev Ayzenberg, Shady Abu-Hussein, Raja Giryes +1
Full data acquisition in MRI is inherently slow, which limits clinical throughput and increases patient discomfort. Compressed Sensing MRI (CS-MRI) seeks to accelerate acquisition…
ID-LoRA: Identity-Driven Audio-Video Personalization with In-Context LoRA
Aviad Dahan, Moran Yanuka, Noa Kraicer +2
Existing video personalization methods preserve visual likeness but treat video and audio separately. Without access to the visual scene, audio models cannot synchronize sounds wit…
On the Relation Between Linear Diffusion and Power Iteration
Dana Weitzner, Mauricio Delbracio, Peyman Milanfar +1
Recently, diffusion models have gained popularity due to their impressive generative abilities. These models learn the implicit distribution given by the training dataset, and samp…
ProtoSAM: One-Shot Medical Image Segmentation With Foundational Models
Lev Ayzenberg, Raja Giryes, Hayit Greenspan
This work introduces a new framework, ProtoSAM, for one-shot medical image segmentation. It combines the use of prototypical networks, known for few-shot segmentation, with SAM - a…
X-ray2CTPA: Leveraging Diffusion Models to Enhance Pulmonary Embolism Classification
Noa Cahan, Eyal Klang, Galit Aviram +4
Chest X-rays or chest radiography (CXR), commonly used for medical diagnostics, typically enables limited imaging compared to computed tomography (CT) scans, which offer more detai…