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Found 15 papers, 10 papers with code
Concept Weaver: Enabling Multi-Concept Fusion in Text-to-Image Models
While there has been significant progress in customizing text-to-image generation models, generating images that combine multiple personalized concepts remains challenging.
Patch-wise Graph Contrastive Learning for Image Translation
Recently, patch-wise contrastive learning is drawing attention for the image translation by exploring the semantic correspondence between the input and output images.
Contrastive Denoising Score for Text-guided Latent Diffusion Image Editing
A promising recent approach in this realm is Delta Denoising Score (DDS) - an image editing technique based on Score Distillation Sampling (SDS) framework that leverages the rich generative prior of text-to-image diffusion models.
ED-NeRF: Efficient Text-Guided Editing of 3D Scene with Latent Space NeRF
These advancements have been extended to 3D models, enabling the generation of novel 3D objects from textual descriptions.
Improving Diffusion-based Image Translation using Asymmetric Gradient Guidance
Diffusion models have shown significant progress in image translation tasks recently.
Unpaired Image-to-Image Translation via Neural Schrödinger Bridge
Diffusion models are a powerful class of generative models which simulate stochastic differential equations (SDEs) to generate data from noise.
Zero-shot Generation of Coherent Storybook from Plain Text Story using Diffusion Models
Recent advancements in large scale text-to-image models have opened new possibilities for guiding the creation of images through human-devised natural language.
Diffusion-based Image Translation using Disentangled Style and Content Representation
Diffusion-based image translation guided by semantic texts or a single target image has enabled flexible style transfer which is not limited to the specific domains.
One-Shot Adaptation of GAN in Just One CLIP
Specifically, our model employs a two-step training strategy: reference image search in the source generator using a CLIP-guided latent optimization, followed by generator fine-tuning with a novel loss function that imposes CLIP space consistency between the source and adapted generators.
Exploring Patch-wise Semantic Relation for Contrastive Learning in Image-to-Image Translation Tasks
Recently, contrastive learning-based image translation methods have been proposed, which contrasts different spatial locations to enhance the spatial correspondence.
CLIPstyler: Image Style Transfer with a Single Text Condition
In order to deal with such applications, we propose a new framework that enables a style transfer `without' a style image, but only with a text description of the desired style.
Diagonal Attention and Style-based GAN for Content-Style Disentanglement in Image Generation and Translation
One of the important research topics in image generative models is to disentangle the spatial contents and styles for their separate control.
Progressive Face Super-Resolution via Attention to Facial Landmark
Face Super-Resolution (SR) is a subfield of the SR domain that specifically targets the reconstruction of face images.
Ranked #1 on
Face Alignment
on CelebA + AFLW Unaligned
Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks
We also train the model to synthesize brain disorder MRI data to demonstrate the wide applicability of our model.
Representation of White- and Black-Box Adversarial Examples in Deep Neural Networks and Humans: A Functional Magnetic Resonance Imaging Study
However, DNNs exhibit idiosyncrasies that suggest their visual representation and processing might be substantially different from human vision.
