6 papers · 1 filter
Discovering Latent Graphs with GFlowNets for Diverse Conditional Image Generation
Bailey Trang, Parham Saremi, Alan Q. Wang +6
Capturing diversity is crucial in conditional and prompt-based image generation, particularly when conditions contain uncertainty that can lead to multiple plausible outputs. To ge…
AURA: A Multi-Modal Medical Agent for Understanding, Reasoning & Annotation
Nima Fathi, Amar Kumar, Tal Arbel
Recent advancements in Large Language Models (LLMs) have catalyzed a paradigm shift from static prediction systems to agentic AI agents capable of reasoning, interacting with tools…
PRISM: High-Resolution & Precise Counterfactual Medical Image Generation using Language-guided Stable Diffusion
Amar Kumar, Anita Kriz, Mohammad Havaei +1
Developing reliable and generalizable deep learning systems for medical imaging faces significant obstacles due to spurious correlations, data imbalances, and limited text annotati…
RL4Med-DDPO: Reinforcement Learning for Controlled Guidance Towards Diverse Medical Image Generation using Vision-Language Foundation Models
Parham Saremi, Amar Kumar, Mohamed Mohamed +2
Vision-Language Foundation Models (VLFM) have shown a tremendous increase in performance in terms of generating high-resolution, photorealistic natural images. While VLFMs show a r…
Language-Guided Trajectory Traversal in Disentangled Stable Diffusion Latent Space for Factorized Medical Image Generation
Zahra TehraniNasab, Amar Kumar, Tal Arbel
Text-to-image diffusion models have demonstrated a remarkable ability to generate photorealistic images from natural language prompts. These high-resolution, language-guided synthe…
Leveraging Vision-Language Foundation Models to Reveal Hidden Image-Attribute Relationships in Medical Imaging
Amar Kumar, Anita Kriz, Barak Pertzov +1
Vision-language foundation models (VLMs) have shown impressive performance in guiding image generation through text, with emerging applications in medical imaging. In this work, we…