Search Results for author:
Found 48 papers, 14 papers with code
Unsupervised Deformable Image Registration with Structural Nonparametric Smoothing
Learning-based deformable image registration (DIR) accelerates alignment by amortizing traditional optimization via neural networks.
Reconsider the Template Mesh in Deep Learning-based Mesh Reconstruction
Mesh reconstruction is a cornerstone process across various applications, including in-silico trials, digital twins, surgical planning, and navigation.
Cross-Modal Interactive Perception Network with Mamba for Lung Tumor Segmentation in PET-CT Images
Lung cancer is a leading cause of cancer-related deaths globally.
Coordinated Energy-Trajectory Economic Model Predictive Control for Autonomous Surface Vehicles under Disturbances
The paper proposes a novel Economic Model Predictive Control (EMPC) scheme for Autonomous Surface Vehicles (ASVs) to simultaneously address path following accuracy and energy constraints under environmental disturbances.
Prompt-driven Transferable Adversarial Attack on Person Re-Identification with Attribute-aware Textual Inversion
Person re-identification (re-id) models are vital in security surveillance systems, requiring transferable adversarial attacks to explore the vulnerabilities of them.
Multi-Keypoint Affordance Representation for Functional Dexterous Grasping
Additionally, we present a Keypoint-based Grasp matrix Transformation (KGT) method, ensuring spatial consistency between hand keypoints and object contact points, thus providing a direct link between visual perception and dexterous grasping actions.
Multiview Point Cloud Registration Based on Minimum Potential Energy for Free-Form Blade Measurement
In this paper, we propose a novel global registration method that is based on the minimum potential energy (MPE) method to address these problems.
Modality Unified Attack for Omni-Modality Person Re-Identification
Specifically, we propose a novel Modality Unified Attack method to train modality-specific adversarial generators to generate AEs that effectively attack different omni-modality models.
Hierarchical Gaussian Mixture Model Splatting for Efficient and Part Controllable 3D Generation
3D content creation has achieved significant progress in terms of both quality and speed.
Feature Information Driven Position Gaussian Distribution Estimation for Tiny Object Detection
Taking the information map as prior knowledge guidance, we construct a multi-scale position gaussian distribution map prediction module, simultaneously modulating the information map and distribution map to focus on tiny objects during training.
Tacit Learning with Adaptive Information Selection for Cooperative Multi-Agent Reinforcement Learning
In multi-agent reinforcement learning (MARL), the centralized training with decentralized execution (CTDE) framework has gained widespread adoption due to its strong performance.
EADReg: Probabilistic Correspondence Generation with Efficient Autoregressive Diffusion Model for Outdoor Point Cloud Registration
To address this issue, we propose a novel framework named EADReg for efficient and robust registration of LiDAR point clouds based on autoregressive diffusion models.
A Polarization Image Dehazing Method Based on the Principle of Physical Diffusion
Computer vision is increasingly used in areas such as unmanned vehicles, surveillance systems and remote sensing.
Fidelity-Imposed Displacement Editing for the Learn2Reg 2024 SHG-BF Challenge
Co-examination of second-harmonic generation (SHG) and bright-field (BF) microscopy enables the differentiation of tissue components and collagen fibers, aiding the analysis of human breast and pancreatic cancer tissues.
Unsupervised Multimodal 3D Medical Image Registration with Multilevel Correlation Balanced Optimization
However, due to the differences between multimodal images and intraoperative image deformation caused by tissue displacement and removal during surgery, effective registration of preoperative and intraoperative multimodal images faces significant challenges.
Learning to Learn Transferable Generative Attack for Person Re-Identification
Deep learning-based person re-identification (re-id) models are widely employed in surveillance systems and inevitably inherit the vulnerability of deep networks to adversarial attacks.
Large Scale Unsupervised Brain MRI Image Registration Solution for Learn2Reg 2024
In this paper, we summarize the methods and experimental results we proposed for Task 2 in the learn2reg 2024 Challenge.
CVPT: Cross-Attention help Visual Prompt Tuning adapt visual task
These methods optimize large-scale pre-trained models for specific tasks by fine-tuning a select group of parameters.
Learning Granularity-Aware Affordances from Human-Object Interaction for Tool-Based Functional Grasping in Dexterous Robotics
To address this, we propose a granularity-aware affordance feature extraction method for locating functional affordance areas and predicting dexterous coarse gestures.
Teaching with Uncertainty: Unleashing the Potential of Knowledge Distillation in Object Detection
In this paper, we propose a novel feature-based distillation paradigm with knowledge uncertainty for object detection, termed "Uncertainty Estimation-Discriminative Knowledge Extraction-Knowledge Transfer (UET)", which can seamlessly integrate with existing distillation methods.
Quantum Adjoint Convolutional Layers for Effective Data Representation
Quantum Convolutional Layer (QCL) is considered as one of the core of Quantum Convolutional Neural Networks (QCNNs) due to its efficient data feature extraction capability.
3D Object Detection from Point Cloud via Voting Step Diffusion
In this work, we focus on the distributional properties of point clouds and formulate the voting process as generating new points in the high-density region of the distribution of object centers.
External Knowledge Enhanced 3D Scene Generation from Sketch
Generating realistic 3D scenes is challenging due to the complexity of room layouts and object geometries. We propose a sketch based knowledge enhanced diffusion architecture (SEK) for generating customized, diverse, and plausible 3D scenes.
Learning Coherent Matrixized Representation in Latent Space for Volumetric 4D Generation
Directly learning to model 4D content, including shape, color, and motion, is challenging.
Diffusion-Driven Self-Supervised Learning for Shape Reconstruction and Pose Estimation
Furthermore, we introduce a pretrain-to-refine self-supervised training paradigm to train our network.
Towards Real-World Aerial Vision Guidance with Categorical 6D Pose Tracker
In this paper, we investigate the real-world robot task of aerial vision guidance for aerial robotics manipulation, utilizing category-level 6-DoF pose tracking.
Learn Once Plan Arbitrarily (LOPA): Attention-Enhanced Deep Reinforcement Learning Method for Global Path Planning
However, when dealing with global planning tasks, these methods face serious challenges such as poor convergence and generalization.
L4D-Track: Language-to-4D Modeling Towards 6-DoF Tracking and Shape Reconstruction in 3D Point Cloud Stream
To this end we present a generic Language-to-4D modeling paradigm termed L4D-Track that tackles zero-shot 6-DoF \underline Track ing and shape reconstruction by learning pairwise implicit 3D representation and multi-level multi-modal alignment.
Sketch and Text Guided Diffusion Model for Colored Point Cloud Generation
In this paper, we propose a sketch and text guided probabilistic diffusion model for colored point cloud generation that conditions the denoising process jointly with a hand drawn sketch of the object and its textual description.
OAFuser: Towards Omni-Aperture Fusion for Light Field Semantic Segmentation
To simultaneously streamline the redundant information from the light field cameras and avoid feature loss during network propagation, we present a simple yet very effective Sub-Aperture Fusion Module (SAFM).
Towards Anytime Optical Flow Estimation with Event Cameras
Event cameras respond to changes in log-brightness at the millisecond level, making them ideal for optical flow estimation.
Towards Source-free Domain Adaptive Semantic Segmentation via Importance-aware and Prototype-contrast Learning
It utilizes a well-trained source model and unlabeled target data to achieve adaptation in the target domain.
A Graph Reconstruction by Dynamic Signal Coefficient for Fault Classification
To improve the performance in identifying the faults under strong noise for rotating machinery, this paper presents a dynamic feature reconstruction signal graph method, which plays the key role of the proposed end-to-end fault diagnosis model.
A Signed Subgraph Encoding Approach via Linear Optimization for Link Sign Prediction
In this paper, we propose a different link sign prediction architecture call SELO (Subgraph Encoding via Linear Optimization), which obtains overall leading prediction performances compared the state-of-the-art algorithm SDGNN.
Learning from Pixel-Level Noisy Label : A New Perspective for Light Field Saliency Detection
Saliency detection with light field images is becoming attractive given the abundant cues available, however, this comes at the expense of large-scale pixel level annotated data which is expensive to generate.
TransKD: Transformer Knowledge Distillation for Efficient Semantic Segmentation
Furthermore, we introduce two optimization modules to enhance the patch embedding distillation from different perspectives: (1) Global-Local Context Mixer (GL-Mixer) extracts both global and local information of a representative embedding; (2) Embedding Assistant (EA) acts as an embedding method to seamlessly bridge teacher and student models with the teacher's number of channels.
Learning From Pixel-Level Noisy Label: A New Perspective for Light Field Saliency Detection
Saliency detection with light field images is becoming attractive given the abundant cues available, however, this comes at the expense of large-scale pixel level annotated data which is expensive to generate.
Projected Sliced Wasserstein Autoencoder-based Hyperspectral Images Anomaly Detection
In this case, the Wasserstein distance can be calculated with the closed-form, even the prior distribution is not Gaussian.
Multi-Expert Adversarial Attack Detection in Person Re-identification Using Context Inconsistency
The success of deep neural networks (DNNs) has promoted the widespread applications of person re-identification (ReID).
Free-form Description Guided 3D Visual Graph Network for Object Grounding in Point Cloud
There are three main challenges in 3D object grounding: to find the main focus in the complex and diverse description; to understand the point cloud scene; and to locate the target object.
Learning Person Re-identification Models from Videos with Weak Supervision
In order to cope with this issue, we introduce the problem of learning person re-identification models from videos with weak supervision.
Multiple Instance Learning
Video-Based Person Re-Identification
Minimum Potential Energy of Point Cloud for Robust Global Registration
Different from the most existing point set registration methods which usually extract the descriptors to find correspondences between point sets, our proposed MPE alignment method is able to handle large scale raw data offset without depending on traditional descriptors extraction, whether for the local or global registration methods.
Relation Graph Network for 3D Object Detection in Point Clouds
Convolutional Neural Networks (CNNs) have emerged as a powerful strategy for most object detection tasks on 2D images.
Artificial intelligence empowered multi-AGVs in manufacturing systems
AGVs are driverless robotic vehicles that picks up and delivers materials.
Exploiting Global Camera Network Constraints for Unsupervised Video Person Re-identification
Additionally, a cross-view matching strategy followed by global camera network constraints is proposed to explore the matching relationships across the entire camera network.
Partial Labeled Gastric Tumor Segmentation via patch-based Reiterative Learning
Gastric cancer is the second leading cause of cancer-related deaths worldwide, and the major hurdle in biomedical image analysis is the determination of the cancer extent.
3D Face Reconstruction from Light Field Images: A Model-free Approach
Reconstructing 3D facial geometry from a single RGB image has recently instigated wide research interest.
Pulling back error to the hidden-node parameter technology: Single-hidden-layer feedforward network without output weight
SLFNs are universal approximators when at least the parameters of the networks including hidden-node parameter and output weight are exist.
