Search Results for author:
Found 42 papers, 17 papers with code
LayerCraft: Enhancing Text-to-Image Generation with CoT Reasoning and Layered Object Integration
Text-to-image generation (T2I) has become a key area of research with broad applications.
Unsupervised Self-Prior Embedding Neural Representation for Iterative Sparse-View CT Reconstruction
Emerging unsupervised implicit neural representation (INR) methods, such as NeRP, NeAT, and SCOPE, have shown great potential to address sparse-view computed tomography (SVCT) inverse problems.
Search-o1: Agentic Search-Enhanced Large Reasoning Models
To address this limitation, we introduce \textbf{Search-o1}, a framework that enhances LRMs with an agentic retrieval-augmented generation (RAG) mechanism and a Reason-in-Documents module for refining retrieved documents.
Ranked #1 on
Mathematical Reasoning
on MATH500
Unsupervised Multi-Parameter Inverse Solving for Reducing Ring Artifacts in 3D X-Ray CBCT
Ring artifacts are prevalent in 3D cone-beam computed tomography (CBCT) due to non-ideal responses of X-ray detectors, severely degrading imaging quality and reliability.
Resolution Enhancement of Under-sampled Photoacoustic Microscopy Images using Implicit Neural Representations
Acoustic-Resolution Photoacoustic Microscopy (AR-PAM) is promising for subcutaneous vascular imaging, but its spatial resolution is constrained by the Point Spread Function (PSF).
Coordinate-Based Neural Representation Enabling Zero-Shot Learning for 3D Multiparametric Quantitative MRI
Quantitative magnetic resonance imaging (qMRI) offers tissue-specific physical parameters with significant potential for neuroscience research and clinical practice.
Moner: Motion Correction in Undersampled Radial MRI with Unsupervised Neural Representation
Motion correction (MoCo) in radial MRI is a challenging problem due to the unpredictability of subject's motion.
Limited-View Photoacoustic Imaging Reconstruction Via High-quality Self-supervised Neural Representation
In this study, we introduce a self-supervised network termed HIgh-quality Self-supervised neural representation (HIS), which tackles the inverse problem of photoacoustic imaging to reconstruct high-quality photoacoustic images from sensor data acquired under limited viewpoints.
Highly Accelerated MRI via Implicit Neural Representation Guided Posterior Sampling of Diffusion Models
The posterior sampling of diffusion models based on the real measurement data holds significant promise of improved reconstruction accuracy.
Enabling Discriminative Reasoning in LLMs for Legal Judgment Prediction
Legal judgment prediction is essential for enhancing judicial efficiency.
Sparse-view Signal-domain Photoacoustic Tomography Reconstruction Method Based on Neural Representation
Photoacoustic tomography is a hybrid biomedical technology, which combines the advantages of acoustic and optical imaging.
Zero-Shot Image Denoising for High-Resolution Electron Microscopy
Noise2SR trains the network with paired noisy images of different resolutions, which is conducted via SR strategy.
VP-LLM: Text-Driven 3D Volume Completion with Large Language Models through Patchification
Recent conditional 3D completion works have mainly relied on CLIP or BERT to encode textual information, which cannot support complex instruction.
FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research
Our toolkit and resources are available at https://github. com/RUC-NLPIR/FlashRAG.
Semantic Gesticulator: Semantics-Aware Co-Speech Gesture Synthesis
In this work, we present Semantic Gesticulator, a novel framework designed to synthesize realistic gestures accompanying speech with strong semantic correspondence.
Solving Energy-Independent Density for CT Metal Artifact Reduction via Neural Representation
In this work, we propose Density neural representation (Diner), a novel unsupervised MAR method.
DPER: Diffusion Prior Driven Neural Representation for Limited Angle and Sparse View CT Reconstruction
Additionally, it effectively augments the feasibility of the solution space for the inverse problem through the generative diffusion model, resulting in increased stability and precision in the solutions.
From Matching to Generation: A Survey on Generative Information Retrieval
Based on the form of information provided to users, current research in GenIR can be categorized into two aspects: \textbf{(1) Generative Document Retrieval} (GR) leverages the generative model's parameters for memorizing documents, enabling retrieval by directly generating relevant document identifiers without explicit indexing.
Neural Implicit Surface Reconstruction of Freehand 3D Ultrasound Volume with Geometric Constraints
Three-dimensional (3D) freehand ultrasound (US) is a widely used imaging modality that allows non-invasive imaging of medical anatomy without radiation exposure.
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.
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.
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.
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.
Ranked #3 on
Hand Gesture Recognition
on ChaLearn val
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).
