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Found 11 papers, 5 papers with code
PALP: Prompt Aligned Personalization of Text-to-Image Models
We term our approach prompt-aligned personalization.
AnyLens: A Generative Diffusion Model with Any Rendering Lens
State-of-the-art diffusion models can generate highly realistic images based on various conditioning like text, segmentation, and depth.
The Chosen One: Consistent Characters in Text-to-Image Diffusion Models
Our quantitative analysis demonstrates that our method strikes a better balance between prompt alignment and identity consistency compared to the baseline methods, and these findings are reinforced by a user study.
Domain-Agnostic Tuning-Encoder for Fast Personalization of Text-To-Image Models
Text-to-image (T2I) personalization allows users to guide the creative image generation process by combining their own visual concepts in natural language prompts.
Encoder-based Domain Tuning for Fast Personalization of Text-to-Image Models
Specifically, we employ two components: First, an encoder that takes as an input a single image of a target concept from a given domain, e. g. a specific face, and learns to map it into a word-embedding representing the concept.
Single Motion Diffusion
We harness the power of diffusion models and present a denoising network explicitly designed for the task of learning from a single input motion.
Learned Queries for Efficient Local Attention
Vision Transformers (ViT) serve as powerful vision models.
Ranked #362 on
Image Classification
on ImageNet
InAugment: Improving Classifiers via Internal Augmentation
Image augmentation techniques apply transformation functions such as rotation, shearing, or color distortion on an input image.
Focus-and-Expand: Training Guidance Through Gradual Manipulation of Input Features
Drawing inspiration from Parameter Continuation methods, we propose steering the training process to consider specific features in the input more than others, through gradual shifts in the input domain.
Unsupervised Multi-Modal Image Registration via Geometry Preserving Image-to-Image Translation
In this work, we bypass the difficulties of developing cross-modality similarity measures, by training an image-to-image translation network on the two input modalities.
Image Resizing by Reconstruction from Deep Features
In this paper we perform image resizing in feature space where the deep layers of a neural network contain rich important semantic information.
