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Found 12 papers, 7 papers with code
CLoRA: A Contrastive Approach to Compose Multiple LoRA Models
Low-Rank Adaptations (LoRAs) have emerged as a powerful and popular technique in the field of image generation, offering a highly effective way to adapt and refine pre-trained deep learning models for specific tasks without the need for comprehensive retraining.
A foundation model utilizing chest CT volumes and radiology reports for supervised-level zero-shot detection of abnormalities
A major challenge in computational research in 3D medical imaging is the lack of comprehensive datasets.
LIME: Localized Image Editing via Attention Regularization in Diffusion Models
Diffusion models (DMs) have gained prominence due to their ability to generate high-quality, varied images, with recent advancements in text-to-image generation.
CONFORM: Contrast is All You Need For High-Fidelity Text-to-Image Diffusion Models
Images produced by text-to-image diffusion models might not always faithfully represent the semantic intent of the provided text prompt, where the model might overlook or entirely fail to produce certain objects.
DENTEX: An Abnormal Tooth Detection with Dental Enumeration and Diagnosis Benchmark for Panoramic X-rays
To address these issues, the Dental Enumeration and Diagnosis on Panoramic X-rays Challenge (DENTEX) has been organized in association with the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) in 2023.
GenerateCT: Text-Conditional Generation of 3D Chest CT Volumes
As an example, we generated 100, 000 3D CT volumes, fivefold the number in our real dataset, and trained the classifier exclusively on these synthetic volumes.
Diffusion-Based Hierarchical Multi-Label Object Detection to Analyze Panoramic Dental X-rays
To learn from all three hierarchies jointly, we introduce a novel diffusion-based hierarchical multi-label object detection framework by adapting a diffusion-based method that formulates object detection as a denoising diffusion process from noisy boxes to object boxes.
LatentSwap3D: Semantic Edits on 3D Image GANs
3D GANs have the ability to generate latent codes for entire 3D volumes rather than only 2D images.
Fantastic Style Channels and Where to Find Them: A Submodular Framework for Discovering Diverse Directions in GANs
We evaluate our framework with qualitative and quantitative experiments and show that our method finds more diverse and disentangled directions.
Object-aware Monocular Depth Prediction with Instance Convolutions
With the advent of deep learning, estimating depth from a single RGB image has recently received a lot of attention, being capable of empowering many different applications ranging from path planning for robotics to computational cinematography.
Graph2Pix: A Graph-Based Image to Image Translation Framework
In this paper, we propose a graph-based image-to-image translation framework for generating images.
LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions
Recent research has shown that it is possible to find interpretable directions in the latent spaces of pre-trained Generative Adversarial Networks (GANs).
