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Found 24 papers, 9 papers with code
From Matching to Generation: A Survey on Generative Information Retrieval
We will summarize the advancements in GR regarding model training, document identifier, incremental learning, downstream tasks adaptation, multi-modal GR and generative recommendation, as well as progress in reliable response generation in aspects of internal knowledge memorization, external knowledge augmentation, generating response with citations and personal information assistant.
Prompt2NeRF-PIL: Fast NeRF Generation via Pretrained Implicit Latent
This paper explores promptable NeRF generation (e. g., text prompt or single image prompt) for direct conditioning and fast generation of NeRF parameters for the underlying 3D scenes, thus undoing complex intermediate steps while providing full 3D generation with conditional control.
IMJENSE: Scan-specific Implicit Representation for Joint Coil Sensitivity and Image Estimation in Parallel MRI
Benefiting from the powerful continuous representation and joint estimation of the MRI image and coil sensitivities, IMJENSE outperforms conventional image or k-space domain reconstruction algorithms.
JSMoCo: Joint Coil Sensitivity and Motion Correction in Parallel MRI with a Self-Calibrating Score-Based Diffusion Model
In this work, we propose to jointly estimate the motion parameters and coil sensitivity maps for under-sampled MRI reconstruction, referred to as JSMoCo.
Self-Prompting Large Vision Models for Few-Shot Medical Image Segmentation
Recent advancements in large foundation models have shown promising potential in the medical industry due to their flexible prompting capability.
Unsupervised Polychromatic Neural Representation for CT Metal Artifact Reduction
In this work, we present a novel Polychromatic neural representation (Polyner) to tackle the challenging problem of CT imaging when metallic implants exist within the human body.
Mining fMRI Dynamics with Parcellation Prior for Brain Disease Diagnosis
To characterize atypical brain dynamics under diseases, prevalent studies investigate functional magnetic resonance imaging (fMRI).
Self-supervised arbitrary scale super-resolution framework for anisotropic MRI
The anisotropic volume's high-resolution (HR) plane is used to build the HR-LR image pairs for model training.
Spatiotemporal implicit neural representation for unsupervised dynamic MRI reconstruction
The high-quality and inner continuity of the images provided by INR has great potential to further improve the spatiotemporal resolution of dynamic MRI, without the need of any training data.
Joint Rigid Motion Correction and Sparse-View CT via Self-Calibrating Neural Field
NeRF-based SVCT methods represent the desired CT image as a continuous function of spatial coordinates and train a Multi-Layer Perceptron (MLP) to learn the function by minimizing loss on the SV sinogram.
A scan-specific unsupervised method for parallel MRI reconstruction via implicit neural representation
This function was parameterized by a neural network and learned directly from the measured k-space itself without additional fully sampled high-quality training data.
SCULPTOR: Skeleton-Consistent Face Creation Using a Learned Parametric Generator
Named after the fossils of one of the oldest known human ancestors, our LUCY dataset contains high-quality Computed Tomography (CT) scans of the complete human head before and after orthognathic surgeries, critical for evaluating surgery results.
Continuous longitudinal fetus brain atlas construction via implicit neural representation
Using implicit neural representation, we construct a continuous and noise-free longitudinal fetus brain atlas as a function of the 4D spatial-temporal coordinate.
Noise2SR: Learning to Denoise from Super-Resolved Single Noisy Fluorescence Image
Experimental results of simulated noise and real microscopy noise removal show that Noise2SR outperforms two blind-spot based self-supervised deep learning image denoising methods.
Self-Supervised Coordinate Projection Network for Sparse-View Computed Tomography
Compared with recent related works that solve similar problems using implicit neural representation network (INR), our essential contribution is an effective and simple re-projection strategy that pushes the tomography image reconstruction quality over supervised deep learning CT reconstruction works.
NIMBLE: A Non-rigid Hand Model with Bones and Muscles
Emerging Metaverse applications demand reliable, accurate, and photorealistic reproductions of human hands to perform sophisticated operations as if in the physical world.
Node-Aligned Graph Convolutional Network for Whole-Slide Image Representation and Classification
In this paper, we propose a hierarchical global-to-local clustering strategy to build a Node-Aligned GCN (NAGCN) to represent WSI with rich local structural information as well as global distribution.
An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural Representation
In the ArSSR model, the reconstruction of HR images with different up-scaling rates is defined as learning a continuous implicit voxel function from the observed LR images.
IREM: High-Resolution Magnetic Resonance (MR) Image Reconstruction via Implicit Neural Representation
For collecting high-quality high-resolution (HR) MR image, we propose a novel image reconstruction network named IREM, which is trained on multiple low-resolution (LR) MR images and achieve an arbitrary up-sampling rate for HR image reconstruction.
PIANO: A Parametric Hand Bone Model from Magnetic Resonance Imaging
Hand modeling is critical for immersive VR/AR, action understanding, or human healthcare.
MoDL-QSM: Model-based Deep Learning for Quantitative Susceptibility Mapping
However, there exists a mismatch between the observed phase and the theoretical forward phase estimated by the susceptibility label.
Learning-based Single-step Quantitative Susceptibility Mapping Reconstruction Without Brain Extraction
To address these challenges, we propose a learning-based QSM reconstruction method that directly estimates the magnetic susceptibility from total phase images without the need for brain extraction and background phase removal, referred to as autoQSM.
Scene Flow to Action Map: A New Representation for RGB-D based Action Recognition with Convolutional Neural Networks
Based on the scene flow vectors, we propose a new representation, namely, Scene Flow to Action Map (SFAM), that describes several long term spatio-temporal dynamics for action recognition.
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Hand Gesture Recognition
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Large-scale Continuous Gesture Recognition Using Convolutional Neural Networks
This paper addresses the problem of continuous gesture recognition from sequences of depth maps using convolutional neutral networks (ConvNets).
