8 citations · 20 across the 18 of their papers we have counts for
13 papers · 1 filter
Text-to-Image Models Need Less from Text Encoders Than You Think
Nurit Spingarn, Noa Cohen, Tamar Rott Shaham +1
Text-to-image models rely on text prompts as their primary interface to human intent. Prompts are encoded by a text encoder into embeddings that condition the image generation proc…
Vision-Language Binding in In-Context Image Generation
Chris Ge, Rohit Gandikota, Antonio Torralba +1
In-context image generation models such as FLUX.2 take a text prompt and an optional reference image as visual conditioning for the output. Internally, all three inputs -- text, re…
From Activation to Specificity: Automating Counterfactual Testing of Visual Representations in the Human Brain
Yuval Golbari, Navve Wasserman, Matias Cosarinsky +5
Identifying which brain regions represent a visual concept in the human brain is a central challenge in neuroscience. Existing approaches have localized coarse functional regions (…
The Dual Mechanisms of Spatial Variable Binding in Vision-Language Models
Kelly Cui, Nikhil Prakash, Shoval Messica +4
Many multimodal tasks, such as image captioning and visual question answering, require vision-language models (VLMs) to bind objects with their properties and spatial relations. Ye…
BrainExplore: Large-Scale Discovery of Interpretable Visual Representations in the Human Brain
Navve Wasserman, Matias Cosarinsky, Yuval Golbari +4
Understanding how the human brain represents visual concepts, and in which brain regions these representations are encoded, remains a long-standing challenge. Decades of work have…
Automated Detection of Visual Attribute Reliance with a Self-Reflective Agent
Christy Li, Josep Lopez Camuñas, Jake Thomas Touchet +4
When a vision model performs image recognition, which visual attributes drive its predictions? Detecting unintended reliance on specific visual features is critical for ensuring mo…
SketchAgent: Language-Driven Sequential Sketch Generation
Yael Vinker, Tamar Rott Shaham, Kristine Zheng +3
Sketching serves as a versatile tool for externalizing ideas, enabling rapid exploration and visual communication that spans various disciplines. While artificial systems have driv…
A Vision Check-up for Language Models
Pratyusha Sharma, Tamar Rott Shaham, Manel Baradad +5
What does learning to model relationships between strings teach large language models (LLMs) about the visual world? We systematically evaluate LLMs' abilities to generate and reco…
BlendGAN: Learning and Blending the Internal Distributions of Single Images by Spatial Image-Identity Conditioning
Idan Kligvasser, Tamar Rott Shaham, Noa Alkobi +1
Training a generative model on a single image has drawn significant attention in recent years. Single image generative methods are designed to learn the internal patch distribution…
Spatially-Adaptive Pixelwise Networks for Fast Image Translation
Tamar Rott Shaham, Michael Gharbi, Richard Zhang +2
We introduce a new generator architecture, aimed at fast and efficient high-resolution image-to-image translation. We design the generator to be an extremely lightweight function o…
SinGAN: Learning a Generative Model from a Single Natural Image
Tamar Rott Shaham, Tali Dekel, Tomer Michaeli
We introduce SinGAN, an unconditional generative model that can be learned from a single natural image. Our model is trained to capture the internal distribution of patches within…
Deformation Aware Image Compression
Tamar Rott Shaham, Tomer Michaeli
Lossy compression algorithms aim to compactly encode images in a way which enables to restore them with minimal error. We show that a key limitation of existing algorithms is that…
xUnit: Learning a Spatial Activation Function for Efficient Image Restoration
Idan Kligvasser, Tamar Rott Shaham, Tomer Michaeli
In recent years, deep neural networks (DNNs) achieved unprecedented performance in many low-level vision tasks. However, state-of-the-art results are typically achieved by very dee…