Search Results for author:
Found 67 papers, 32 papers with code
Single-View View Synthesis with Self-Rectified Pseudo-Stereo
In this way, the challenging novel view synthesis process is decoupled into two simpler problems of stereo synthesis and 3D reconstruction.
Human Motion Detection Based on Dual-Graph and Weighted Nuclear Norm Regularizations
In the meanwhile, geometry-based regularizations, such as graph regularizations, can be imposed on the foreground.
Homeomorphic Image Registration via Conformal-Invariant Hyperelastic Regularisation
We propose a novel framework for deformable image registration.
Federated Gradient Matching Pursuit
In particular, federated learning (FL) provides such a solution to learn a shared model while keeping training data at local clients.
RainDiffusion: When Unsupervised Learning Meets Diffusion Models for Real-world Image Deraining
Before answering it, we observe two major obstacles of diffusion models in real-world image deraining: the need for paired training data and the limited utilization of multi-scale rain patterns.
Rethinking Generative Methods for Image Restoration in Physics-based Vision: A Theoretical Analysis from the Perspective of Information
In this study, we try to re-interpret these generative methods for image restoration tasks using information theory.
ImLiDAR: Cross-Sensor Dynamic Message Propagation Network for 3D Object Detection
We propose ImLiDAR, a new 3OD paradigm to narrow the cross-sensor discrepancies by progressively fusing the multi-scale features of camera Images and LiDAR point clouds.
UTOPIC: Uncertainty-aware Overlap Prediction Network for Partial Point Cloud Registration
High-confidence overlap prediction and accurate correspondences are critical for cutting-edge models to align paired point clouds in a partial-to-partial manner.
CSDN: Cross-modal Shape-transfer Dual-refinement Network for Point Cloud Completion
The first module transfers the intrinsic shape characteristics from single images to guide the geometry generation of the missing regions of point clouds, in which we propose IPAdaIN to embed the global features of both the image and the partial point cloud into completion.
Ranked #2 on
Point Cloud Completion
on ShapeNet-ViPC
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.
Test-time Adaptation with Calibration of Medical Image Classification Nets for Label Distribution Shift
In this paper, we propose the first method to tackle label shift for medical image classification, which effectively adapt the model learned from a single training label distribution to arbitrary unknown test label distribution.
A New Dataset and A Baseline Model for Breast Lesion Detection in Ultrasound Videos
Moreover, we learn video-level features to classify the breast lesions of the original video as benign or malignant lesions to further enhance the final breast lesion detection performance in ultrasound videos.
Dynamic Message Propagation Network for RGB-D Salient Object Detection
This paper presents a novel deep neural network framework for RGB-D salient object detection by controlling the message passing between the RGB images and depth maps on the feature level and exploring the long-range semantic contexts and geometric information on both RGB and depth features to infer salient objects.
An Improved Normed-Deformable Convolution for Crowd Counting
However, the heads are not uniformly covered by the sampling points in the deformable convolution, resulting in loss of head information.
AGConv: Adaptive Graph Convolution on 3D Point Clouds
In this paper, we propose Adaptive Graph Convolution (AGConv) for wide applications of point cloud analysis.
XBound-Former: Toward Cross-scale Boundary Modeling in Transformers
Skin lesion segmentation from dermoscopy images is of great significance in the quantitative analysis of skin cancers, which is yet challenging even for dermatologists due to the inherent issues, i. e., considerable size, shape and color variation, and ambiguous boundaries.
UCL-Dehaze: Towards Real-world Image Dehazing via Unsupervised Contrastive Learning
From a different yet new perspective, this paper explores contrastive learning with an adversarial training effort to leverage unpaired real-world hazy and clean images, thus bridging the gap between synthetic and real-world haze is avoided.
Robust Dual-Graph Regularized Moving Object Detection
In the meanwhile, sparsity or smoothness based regularizations, such as total variation and $\ell_1$, can be imposed on the foreground.
Detail-recovery Image Deraining via Dual Sample-augmented Contrastive Learning
To bridge the two domain gaps, we propose a semi-supervised detail-recovery image deraining network (Semi-DRDNet) with dual sample-augmented contrastive learning.
Transformer-empowered Multi-scale Contextual Matching and Aggregation for Multi-contrast MRI Super-resolution
However, existing methods still have two shortcomings: (1) they neglect that the multi-contrast features at different scales contain different anatomical details and hence lack effective mechanisms to match and fuse these features for better reconstruction; and (2) they are still deficient in capturing long-range dependencies, which are essential for the regions with complicated anatomical structures.
Refine-Net: Normal Refinement Neural Network for Noisy Point Clouds
Differently, our network is designed to refine the initial normal of each point by extracting additional information from multiple feature representations.
GeoBi-GNN: Geometry-aware Bi-domain Mesh Denoising via Graph Neural Networks
For the first time, we optimize both positions and normals (i. e., dual domains) in a unified framework of GNN, and show the powerful inter-coordination between the dual domains.
Cross-Patch Dense Contrastive Learning for Semi-Supervised Segmentation of Cellular Nuclei in Histopathologic Images
We study the semi-supervised learning problem, using a few labeled data and a large amount of unlabeled data to train the network, by developing a cross-patch dense contrastive learning framework, to segment cellular nuclei in histopathologic images.
Separated Contrastive Learning for Organ-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation
The SIS is proposed to operate on the image set to rebuild a region set under the guidance of structural information.
Real-time landmark detection for precise endoscopic submucosal dissection via shape-aware relation network
Compared with existing solutions, which either neglect geometric relationships among targeting objects or capture the relationships by using complicated aggregation schemes, the proposed network is capable of achieving satisfactory accuracy while maintaining real-time performance by taking full advantage of the spatial relations among landmarks.
Boundary-aware Transformers for Skin Lesion Segmentation
Skin lesion segmentation from dermoscopy images is of great importance for improving the quantitative analysis of skin cancer.
Ranked #4 on
Lesion Segmentation
on ISIC 2018
T-WaveNet: A Tree-Structured Wavelet Neural Network for Time Series Signal Analysis
In this work, we propose a novel tree-structured wavelet neural network for time series signal analysis, namely T-WaveNet, by taking advantage of an inherent property of various types of signals, known as the dominant frequency range.
Efficient Global-Local Memory for Real-time Instrument Segmentation of Robotic Surgical Video
On the other hand, we develop an active global memory to gather the global semantic correlation in long temporal range to current one, in which we gather the most informative frames derived from model uncertainty and frame similarity.
Adaptive Graph Convolution for Point Cloud Analysis
Convolution on 3D point clouds that generalized from 2D grid-like domains is widely researched yet far from perfect.
Ranked #10 on
3D Point Cloud Classification
on IntrA
Reducing Spatial Labeling Redundancy for Semi-supervised Crowd Counting
In this way, we can transfer the original spatial labeling redundancy caused by individual similarities to effective supervision signals on the unlabeled regions.
From Synthetic to Real: Image Dehazing Collaborating with Unlabeled Real Data
Our DID-Net predicts the three component maps by progressively integrating features across scales, and refines each map by passing an independent refinement network.
Ranked #4 on
Image Dehazing
on Haze4k
Active-set algorithms based statistical inference for shape-restricted generalized additive Cox regression models
In this paper, we introduce the shape-restricted inference to the celebrated Cox regression model (SR-Cox), in which the covariate response is modeled as shape-restricted additive functions.
Direction-aware Feature-level Frequency Decomposition for Single Image Deraining
Second, we further establish communication channels between low-frequency maps and high-frequency maps to interactively capture structures from high-frequency maps and add them back to low-frequency maps and, simultaneously, extract details from low-frequency maps and send them back to high-frequency maps, thereby removing rain streaks while preserving more delicate features in the input image.
Relational Graph Neural Network Design via Progressive Neural Architecture Search
We propose a novel solution to addressing a long-standing dilemma in the representation learning of graph neural networks (GNNs): how to effectively capture and represent useful information embedded in long-distance nodes to improve the performance of nodes with low homophily without leading to performance degradation in nodes with high homophily.
Hand Gesture Recognition Based on a Nonconvex Regularization
Recognition of hand gestures is one of the most fundamental tasks in human-robot interaction.
Triple-cooperative Video Shadow Detection
The bottleneck is the lack of a well-established dataset with high-quality annotations for video shadow detection.
FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space
Federated learning allows distributed medical institutions to collaboratively learn a shared prediction model with privacy protection.
Domain Adaptive Robotic Gesture Recognition with Unsupervised Kinematic-Visual Data Alignment
In this paper, we propose a novel unsupervised domain adaptation framework which can simultaneously transfer multi-modality knowledge, i. e., both kinematic and visual data, from simulator to real robot.
Online Pseudo Label Generation by Hierarchical Cluster Dynamics for Adaptive Person Re-Identification
To tackle the problem, we propose an online pseudo label generation by hierarchical cluster dynamics for adaptive ReID.
Collaborative and Adversarial Learning of Focused and Dispersive Representations for Semi-Supervised Polyp Segmentation
In this paper, we present a novel semi-supervised polyp segmentation via collaborative and adversarial learning of focused and dispersive representations learning model, where focused and dispersive extraction module are used to deal with the diversity of location and shape of polyps.
Research Progress of News Recommendation Methods
News recommendation systems were the earliest research field regarding recommendation systems, and were also the earliest recommendation field to apply the collaborative filtering method.
MBA-RainGAN: Multi-branch Attention Generative Adversarial Network for Mixture of Rain Removal from Single Images
We observe three intriguing phenomenons that, 1) rain is a mixture of raindrops, rain streaks and rainy haze; 2) the depth from the camera determines the degrees of object visibility, where objects nearby and faraway are visually blocked by rain streaks and rainy haze, respectively; and 3) raindrops on the glass randomly affect the object visibility of the whole image space.
Constrained Multi-shape Evolution for Overlapping Cytoplasm Segmentation
In the shape evolution, we compensate intensity deficiency for the segmentation by introducing not only the modeled local shape priors but also global shape priors (clump--level) modeled by considering mutual shape constraints of cytoplasms in the clump.
CNN in CT Image Segmentation: Beyound Loss Function for Expoliting Ground Truth Images
Our insight is that feature maps of two CNNs trained respectively on GT and CT images should be similar on some metric space, because they both are used to describe the same objects for the same purpose.
Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion
We tackle this challenge and propose a novel multimodal segmentation framework which is robust to the absence of imaging modalities.
Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation
In this work, we present a novel unsupervised domain adaptation framework, named as Synergistic Image and Feature Alignment (SIFA), to effectively adapt a segmentation network to an unlabeled target domain.
An Active Learning Approach for Reducing Annotation Cost in Skin Lesion Analysis
In this paper, we present a novel active learning framework for cost-effective skin lesion analysis.
Iterative Hard Thresholding for Low CP-rank Tensor Models
In this paper, we utilize the same tensor version of the Restricted Isometry Property (RIP) to extend these results for tensors with low CANDECOMP/PARAFAC (CP) rank.
Convolutional Neural Network with Median Layers for Denoising Salt-and-Pepper Contaminations
We propose a deep fully convolutional neural network with a new type of layer, named median layer, to restore images contaminated by the salt-and-pepper (s&p) noise.
Multi-Task Recurrent Convolutional Network with Correlation Loss for Surgical Video Analysis
Mutually leveraging both low-level feature sharing and high-level prediction correlating, our MTRCNet-CL method can encourage the interactions between the two tasks to a large extent, and hence can bring about benefits to each other.
Ranked #3 on
Surgical tool detection
on Cholec80
Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound
Our attention module utilizes the attention mechanism to selectively leverage the multilevel features integrated from different layers to refine the features at each individual layer, suppressing the non-prostate noise at shallow layers of the CNN and increasing more prostate details into features at deep layers.
Multi-Kernel Correntropy for Robust Learning
In a recent work, the concept of mixture correntropy (MC) was proposed to improve the learning performance, where the kernel function is a mixture Gaussian kernel, namely a linear combination of several zero-mean Gaussian kernels with different widths.
Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation
Our proposed SIFA is an elegant learning diagram which presents synergistic fusion of adaptations from both image and feature perspectives.
Structure-Aware 3D Hourglass Network for Hand Pose Estimation from Single Depth Image
Experimental results show that the proposed structure-aware 3D hourglass network is able to achieve a mean joint error of 7. 4 mm in MSRA and 8. 9 mm in NYU datasets, respectively.
Bidirectional Feature Pyramid Network with Recurrent Attention Residual Modules for Shadow Detection
Second, we develop a bidirectional feature pyramid network (BFPN) to aggregate shadow contexts spanned across different CNN layers by deploying two series of RAR modules in the network to iteratively combine and refine context features: one series to refine context features from deep to shallow layers, and another series from shallow to deep layers.
Ranked #3 on
Shadow Detection
on SBU
Direction-aware Spatial Context Features for Shadow Detection and Removal
This paper presents a novel deep neural network design for shadow detection and removal by analyzing the spatial image context in a direction-aware manner.
Ranked #6 on
Shadow Removal
on ISTD
SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection
Based on these findings, we present a scale-insensitive convolutional neural network (SINet) for fast detecting vehicles with a large variance of scales.
SFCN-OPI: Detection and Fine-grained Classification of Nuclei Using Sibling FCN with Objectness Prior Interaction
Cell nuclei detection and fine-grained classification have been fundamental yet challenging problems in histopathology image analysis.
Direction-aware Spatial Context Features for Shadow Detection
To achieve this, we first formulate the direction-aware attention mechanism in a spatial recurrent neural network (RNN) by introducing attention weights when aggregating spatial context features in the RNN.
Ranked #2 on
RGB Salient Object Detection
on SBU
Automated Pulmonary Nodule Detection via 3D ConvNets with Online Sample Filtering and Hybrid-Loss Residual Learning
In this paper, we propose a novel framework with 3D convolutional networks (ConvNets) for automated detection of pulmonary nodules from low-dose CT scans, which is a challenging yet crucial task for lung cancer early diagnosis and treatment.
Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets
Second, it avoids learning redundant feature maps by encouraging feature reuse and hence requires fewer parameters to achieve high performance, which is essential for medical applications with limited training data.
ScanNet: A Fast and Dense Scanning Framework for Metastatic Breast Cancer Detection from Whole-Slide Images
Lymph node metastasis is one of the most significant diagnostic indicators in breast cancer, which is traditionally observed under the microscope by pathologists.
Robust Learning with Kernel Mean p-Power Error Loss
Correntropy is a second order statistical measure in kernel space, which has been successfully applied in robust learning and signal processing.
Fine-grained Recurrent Neural Networks for Automatic Prostate Segmentation in Ultrasound Images
Additionally, our approach is general and can be extended to other medical image segmentation tasks, where boundary incompleteness is one of the main challenges.
3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes
Automatic liver segmentation from CT volumes is a crucial prerequisite yet challenging task for computer-aided hepatic disease diagnosis and treatment.
A Multiphase Image Segmentation Based on Fuzzy Membership Functions and L1-norm Fidelity
In this paper, we propose a variational multiphase image segmentation model based on fuzzy membership functions and L1-norm fidelity.
Two-stage Geometric Information Guided Image Reconstruction
Existing methods mostly work well on piecewise constant images but not so well on piecewise smooth images such as natural images, medical images that contain a lot of details.
