5 papers
FlowMo: Variance-Based Flow Guidance for Coherent Motion in Video Generation
Ariel Shaulov, Itay Hazan, Lior Wolf +1
Text-to-video diffusion models are notoriously limited in their ability to model temporal aspects such as motion, physics, and dynamic interactions. Existing approaches address thi…
Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability
Yarden Bakish, Itamar Zimerman, Hila Chefer +1
The development of effective explainability tools for Transformers is a crucial pursuit in deep learning research. One of the most promising approaches in this domain is Layer-wise…
VideoJAM: Joint Appearance-Motion Representations for Enhanced Motion Generation in Video Models
Hila Chefer, Uriel Singer, Amit Zohar +5
Despite tremendous recent progress, generative video models still struggle to capture real-world motion, dynamics, and physics. We show that this limitation arises from the convent…
A Meaningful Perturbation Metric for Evaluating Explainability Methods
Danielle Cohen, Hila Chefer, Lior Wolf
Deep neural networks (DNNs) have demonstrated remarkable success, yet their wide adoption is often hindered by their opaque decision-making. To address this, attribution methods ha…
Discriminative Class Tokens for Text-to-Image Diffusion Models
Idan Schwartz, Vésteinn Snæbjarnarson, Hila Chefer +4
Recent advances in text-to-image diffusion models have enabled the generation of diverse and high-quality images. While impressive, the images often fall short of depicting subtle…