17 citations · 45 across the 11 of their papers we have counts for
14 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…
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…
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…
Still-Moving: Customized Video Generation without Customized Video Data
Hila Chefer, Shiran Zada, Roni Paiss +7
Customizing text-to-image (T2I) models has seen tremendous progress recently, particularly in areas such as personalization, stylization, and conditional generation. However, expan…
Lumiere: A Space-Time Diffusion Model for Video Generation
Omer Bar-Tal, Hila Chefer, Omer Tov +14
We introduce Lumiere -- a text-to-video diffusion model designed for synthesizing videos that portray realistic, diverse and coherent motion -- a pivotal challenge in video synthes…
The Hidden Language of Diffusion Models
Hila Chefer, Oran Lang, Mor Geva +5
Text-to-image diffusion models have demonstrated an unparalleled ability to generate high-quality, diverse images from a textual prompt. However, the internal representations learn…
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…
Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models
Hila Chefer, Yuval Alaluf, Yael Vinker +2
Recent text-to-image generative models have demonstrated an unparalleled ability to generate diverse and creative imagery guided by a target text prompt. While revolutionary, curre…
Optimizing Relevance Maps of Vision Transformers Improves Robustness
Hila Chefer, Idan Schwartz, Lior Wolf
It has been observed that visual classification models often rely mostly on the image background, neglecting the foreground, which hurts their robustness to distribution changes. T…
No Token Left Behind: Explainability-Aided Image Classification and Generation
Roni Paiss, Hila Chefer, Lior Wolf
The application of zero-shot learning in computer vision has been revolutionized by the use of image-text matching models. The most notable example, CLIP, has been widely used for…
Image-Based CLIP-Guided Essence Transfer
Hila Chefer, Sagie Benaim, Roni Paiss +1
We make the distinction between (i) style transfer, in which a source image is manipulated to match the textures and colors of a target image, and (ii) essence transfer, in which o…
Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers
Hila Chefer, Shir Gur, Lior Wolf
Transformers are increasingly dominating multi-modal reasoning tasks, such as visual question answering, achieving state-of-the-art results thanks to their ability to contextualize…
Transformer Interpretability Beyond Attention Visualization
Hila Chefer, Shir Gur, Lior Wolf
Self-attention techniques, and specifically Transformers, are dominating the field of text processing and are becoming increasingly popular in computer vision classification tasks.…