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Found 26 papers, 14 papers with code
Correlation Ratio for Unsupervised Learning of Multi-modal Deformable Registration
This approach involves training a registration network using pairs of moving and fixed images, along with a loss function that combines an image similarity measure and deformation regularization.
SITA: Structurally Imperceptible and Transferable Adversarial Attacks for Stylized Image Generation
However, these methods face challenges such as poor transferability, high computational costs, and the introduction of noticeable noise, which compromises the aesthetic quality of the original artwork.
NexusGS: Sparse View Synthesis with Epipolar Depth Priors in 3D Gaussian Splatting
In this paper, we present NexusGS, a 3DGS-based approach that enhances novel view synthesis from sparse-view images by directly embedding depth information into point clouds, without relying on complex manual regularizations.
Unsupervised learning of spatially varying regularization for diffeomorphic image registration
Spatially varying regularization accommodates the deformation variations that may be necessary for different anatomical regions during deformable image registration.
PersonaMagic: Stage-Regulated High-Fidelity Face Customization with Tandem Equilibrium
Personalized image generation has made significant strides in adapting content to novel concepts.
Unsupervised Learning of Multi-modal Affine Registration for PET/CT
Affine registration plays a crucial role in PET/CT imaging, where aligning PET with CT images is challenging due to their respective functional and anatomical representations.
Task-Oriented Diffusion Inversion for High-Fidelity Text-based Editing
Recent advancements in text-guided diffusion models have unlocked powerful image manipulation capabilities, yet balancing reconstruction fidelity and editability for real images remains a significant challenge.
D$^4$-VTON: Dynamic Semantics Disentangling for Differential Diffusion based Virtual Try-On
In this paper, we introduce D$^4$-VTON, an innovative solution for image-based virtual try-on.
High-order Neighborhoods Know More: HyperGraph Learning Meets Source-free Unsupervised Domain Adaptation
The drawback of these methods includes: 1) the pair-wise relation is limited to exposing the underlying correlations of two more samples, hindering the exploration of the structural information embedded in the target domain; 2) the clustering process only relies on the semantic feature, while overlooking the critical effect of domain shift, i. e., the distribution differences between the source and target domains.
From Registration Uncertainty to Segmentation Uncertainty
Here, we propose a novel framework to concurrently estimate both the epistemic and aleatoric segmentation uncertainties for image registration.
D3still: Decoupled Differential Distillation for Asymmetric Image Retrieval
Existing methods for asymmetric image retrieval employ a rigid pairwise similarity constraint between the query network and the larger gallery network.
One-for-All: Towards Universal Domain Translation with a Single StyleGAN
In this paper, we propose a novel translation model, UniTranslator, for transforming representations between visually distinct domains under conditions of limited training data and significant visual differences.
A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyond
Deep learning technologies have dramatically reshaped the field of medical image registration over the past decade.
Curricular Contrastive Regularization for Physics-aware Single Image Dehazing
In this paper, we propose a novel curricular contrastive regularization targeted at a consensual contrastive space as opposed to a non-consensual one.
Ranked #3 on
Image Dehazing
on SOTS Indoor
Spatially-varying Regularization with Conditional Transformer for Unsupervised Image Registration
In the past, optimization-based registration models have used spatially-varying regularization to account for deformation variations in different image regions.
Deformable Cross-Attention Transformer for Medical Image Registration
Transformers have recently shown promise for medical image applications, leading to an increasing interest in developing such models for medical image registration.
Editing Out-of-domain GAN Inversion via Differential Activations
Consequently, in the decomposition phase, we further present a GAN prior based deghosting network for separating the final fine edited image from the coarse reconstruction.
TransMorph: Transformer for unsupervised medical image registration
Recently Vision Transformer architectures have been proposed to address the shortcomings of ConvNets and have produced state-of-the-art performances in many medical imaging applications.
Ranked #1 on
Medical Image Registration
on OASIS
From Continuity to Editability: Inverting GANs with Consecutive Images
This inborn property is used for two unique purposes: 1) regularizing the joint inversion process, such that each of the inverted code is semantically accessible from one of the other and fastened in a editable domain; 2) enforcing inter-image coherence, such that the fidelity of each inverted code can be maximized with the complement of other images.
Learning From the Master: Distilling Cross-Modal Advanced Knowledge for Lip Reading
To these ends, we introduce a trainable "master" network which ingests both audio signals and silent lip videos instead of a pretrained teacher.
Mask-ShadowNet: Towards Shadow Removal via Masked Adaptive Instance Normalization
Shadow removal is an important yet challenging task in image processing and computer vision.
Learning Fuzzy Clustering for SPECT/CT Segmentation via Convolutional Neural Networks
The results demonstrated that the proposed method provides fast and robust lesion and bone segmentation for QBSPECT/CT.
ViT-V-Net: Vision Transformer for Unsupervised Volumetric Medical Image Registration
However, the performances of ConvNets are still limited by lacking the understanding of long-range spatial relations in an image.
Ranked #4 on
Medical Image Registration
on OASIS
Invertible Grayscale via Dual Features Ensemble
In this paper, we propose to embed color information into an invertible grayscale, such that it can be easily recovered to the original color.
Generating Anthropomorphic Phantoms Using Fully Unsupervised Deformable Image Registration with Convolutional Neural Networks
In this work, we present a novel image registration method for creating highly anatomically detailed anthropomorphic phantoms from a single digital phantom.
Hierarchical recurrent neural network for skeleton based action recognition
Traditional methods generally model the spatial structure and temporal dynamics of human skeleton with hand-crafted features and recognize human actions by well-designed classifiers.
Ranked #131 on
Skeleton Based Action Recognition
on NTU RGB+D
