Search Results for author:
Found 33 papers, 20 papers with code
NeRF2Points: Large-Scale Point Cloud Generation From Street Views' Radiance Field Optimization
Neural Radiance Fields (NeRF) have emerged as a paradigm-shifting methodology for the photorealistic rendering of objects and environments, enabling the synthesis of novel viewpoints with remarkable fidelity.
ConRF: Zero-shot Stylization of 3D Scenes with Conditioned Radiation Fields
Most of the existing works on arbitrary 3D NeRF style transfer required retraining on each single style condition.
CTNeRF: Cross-Time Transformer for Dynamic Neural Radiance Field from Monocular Video
The goal of our work is to generate high-quality novel views from monocular videos of complex and dynamic scenes.
Federated Learning via Input-Output Collaborative Distillation
Federated learning (FL) is a machine learning paradigm in which distributed local nodes collaboratively train a central model without sharing individually held private data.
UniHead: Unifying Multi-Perception for Detection Heads
More precisely, our approach (1) introduces deformation perception, enabling the model to adaptively sample object features; (2) proposes a Dual-axial Aggregation Transformer (DAT) to adeptly model long-range dependencies, thereby achieving global perception; and (3) devises a Cross-task Interaction Transformer (CIT) that facilitates interaction between the classification and localization branches, thus aligning the two tasks.
Automatic view plane prescription for cardiac magnetic resonance imaging via supervision by spatial relationship between views
Then, a multi-view planning strategy is proposed to aggregate information from the predicted heatmaps for all the source views of a target plane, for a globally optimal prescription, mimicking the similar strategy practiced by skilled human prescribers.
DS-Depth: Dynamic and Static Depth Estimation via a Fusion Cost Volume
Nevertheless, the dynamic cost volume inevitably generates extra occlusions and noise, thus we alleviate this by designing a fusion module that makes static and dynamic cost volumes compensate for each other.
BoxDiff: Text-to-Image Synthesis with Training-Free Box-Constrained Diffusion
As such paired data is time-consuming and labor-intensive to acquire and restricted to a closed set, this potentially becomes the bottleneck for applications in an open world.
Ranked #5 on
Conditional Text-to-Image Synthesis
on COCO-MIG
K-Space-Aware Cross-Modality Score for Synthesized Neuroimage Quality Assessment
To further reflect the frequency-specific information from the magnetic resonance imaging principles, both k-space features and vision features are obtained and employed in our comprehensive encoders with a frequency reconstruction penalty.
Dynamically Masked Discriminator for Generative Adversarial Networks
By treating the generated data in training as a stream, we propose to detect whether the discriminator slows down the learning of new knowledge in generated data.
Cross-Modal Vertical Federated Learning for MRI Reconstruction
To this end, we formulate this practical-yet-challenging cross-modal vertical federated learning task, in which shape data from multiple hospitals have different modalities with a small amount of multi-modality data collected from the same individuals.
Improving GAN Training via Feature Space Shrinkage
The evaluation results demonstrate that our AdaptiveMix can facilitate the training of GANs and effectively improve the image quality of generated samples.
Interactive Segmentation as Gaussian Process Classification
Against this issue, in this paper, we propose to formulate the IS task as a Gaussian process (GP)-based pixel-wise binary classification model on each image.
FemtoDet: An Object Detection Baseline for Energy Versus Performance Tradeoffs
Efficient detectors for edge devices are often optimized for parameters or speed count metrics, which remain in weak correlation with the energy of detectors.
Ranked #30 on
Object Detection
on PASCAL VOC 2007
A New Perspective to Boost Vision Transformer for Medical Image Classification
The experimental results validate the superiority of our BOLT for medical image classification, compared to ImageNet pretrained weights and state-of-the-art self-supervised learning approaches.
SemiCVT: Semi-Supervised Convolutional Vision Transformer for Semantic Segmentation
Semi-supervised learning improves data efficiency of deep models by leveraging unlabeled samples to alleviate the reliance on a large set of labeled samples.
Interactive Segmentation As Gaussion Process Classification
Against this issue, in this paper, we propose to formulate the IS task as a Gaussian process (GP)-based pixel-wise binary classification model on each image.
AdaptiveMix: Improving GAN Training via Feature Space Shrinkage
The evaluation results demonstrate that our AdaptiveMix can facilitate the training of GANs and effectively improve the image quality of generated samples.
Orientation-Shared Convolution Representation for CT Metal Artifact Learning
During X-ray computed tomography (CT) scanning, metallic implants carrying with patients often lead to adverse artifacts in the captured CT images and then impair the clinical treatment.
Knowing the Past to Predict the Future: Reinforcement Virtual Learning
Reinforcement Learning (RL)-based control system has received considerable attention in recent decades.
Decoupled Mixup for Generalized Visual Recognition
Since the observation is that noise-prone regions such as textural and clutter backgrounds are adverse to the generalization ability of CNN models during training, we enhance features from discriminative regions and suppress noise-prone ones when combining an image pair.
A Benchmark for Weakly Semi-Supervised Abnormality Localization in Chest X-Rays
Such a point annotation setting can provide weakly instance-level information for abnormality localization with a marginal annotation cost.
Combating Mode Collapse in GANs via Manifold Entropy Estimation
In this paper, we propose a novel training pipeline to address the mode collapse issue of GANs.
mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation
Concretely, we propose a novel multimodal Medical Transformer (mmFormer) for incomplete multimodal learning with three main components: the hybrid modality-specific encoders that bridge a convolutional encoder and an intra-modal Transformer for both local and global context modeling within each modality; an inter-modal Transformer to build and align the long-range correlations across modalities for modality-invariant features with global semantics corresponding to tumor region; a decoder that performs a progressive up-sampling and fusion with the modality-invariant features to generate robust segmentation.
Ranked #65 on
Semantic Segmentation
on NYU Depth v2
Cross-Modality Neuroimage Synthesis: A Survey
This is followed by a stepwise in-depth analysis to evaluate how cross-modality neuroimage synthesis improves the performance of its downstream tasks.
FedMed-ATL: Misaligned Unpaired Brain Image Synthesis via Affine Transform Loss
The proposed method demonstrates the advanced performance in both the quality of our synthesized results under a severely misaligned and unpaired data setting, and better stability than other GAN-based algorithms.
FedMed-GAN: Federated Domain Translation on Unsupervised Cross-Modality Brain Image Synthesis
There is a clear need to launch a federated learning and facilitate the integration of the dispersed data from different institutions.
GuidedMix-Net: Semi-supervised Semantic Segmentation by Using Labeled Images as Reference
In this paper, we propose a novel method for semi-supervised semantic segmentation named GuidedMix-Net, by leveraging labeled information to guide the learning of unlabeled instances.
GuidedMix-Net: Learning to Improve Pseudo Masks Using Labeled Images as Reference
To take advantage of the labeled examples and guide unlabeled data learning, we further propose a mask generation module to generate high-quality pseudo masks for the unlabeled data.
Brain Image Synthesis With Unsupervised Multivariate Canonical CSCl4Net
Recent advances in neuroscience have highlighted the effectiveness of multi-modal medical data for investigating certain pathologies and understanding human cognition.
Brain Image Synthesis with Unsupervised Multivariate Canonical CSC$\ell_4$Net
Recent advances in neuroscience have highlighted the effectiveness of multi-modal medical data for investigating certain pathologies and understanding human cognition.
DOTE: Dual cOnvolutional filTer lEarning for Super-Resolution and Cross-Modality Synthesis in MRI
Cross-modal image synthesis is a topical problem in medical image computing.
Simultaneous Super-Resolution and Cross-Modality Synthesis of 3D Medical Images using Weakly-Supervised Joint Convolutional Sparse Coding
We propose the weakly-supervised joint convolutional sparse coding to simultaneously solve the problems of super-resolution (SR) and cross-modality image synthesis.
