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Found 20 papers, 2 papers with code
LiDAR Point Cloud-based Multiple Vehicle Tracking with Probabilistic Measurement-Region Association
Simulation results illustrate the superior estimation accuracy of the proposed PMRA-PMBM filter in terms of both positions and extents of the vehicles comparing with PMBM filters using the gamma Gaussian inverse Wishart and DRA implementations.
Low-Multi-Rank High-Order Bayesian Robust Tensor Factorization
Additionally, in the t-SVD framework, the multi-rank of a tensor can describe more fine-grained low-rank structure in the tensor compared with the tubal rank.
Unpaired MRI Super Resolution with Contrastive Learning
To this end, we propose an unpaired MRI SR approach that employs contrastive learning to enhance SR performance with limited HR training data.
V2X Cooperative Perception for Autonomous Driving: Recent Advances and Challenges
To address this gap, this paper provides a comprehensive overview of the evolution of CP technologies, spanning from early explorations to recent developments, including advancements in V2X communication technologies.
Implicit Neural Representation for MRI Parallel Imaging Reconstruction
Our approach represents reconstructed fully-sampled images as functions of voxel coordinates and prior feature vectors from undersampled images, addressing the generalization challenges of INR.
Which Framework is Suitable for Online 3D Multi-Object Tracking for Autonomous Driving with Automotive 4D Imaging Radar?
These provide the first benchmark and important insights for the future development of 4D imaging radar-based online 3D MOT algorithms.
LEGO: Learning and Graph-Optimized Modular Tracker for Online Multi-Object Tracking with Point Clouds
The state-of-the-art approaches usually employ a tracking-by-detection method, and data association plays a critical role.
SMURF: Spatial Multi-Representation Fusion for 3D Object Detection with 4D Imaging Radar
Additionally, KDE helps alleviate point cloud sparsity by capturing density features.
LXL: LiDAR Excluded Lean 3D Object Detection with 4D Imaging Radar and Camera Fusion
They are sent to the core of LXL, called "radar occupancy-assisted depth-based sampling", to aid image view transformation.
UNAEN: Unsupervised Abnormality Extraction Network for MRI Motion Artifact Reduction
Our results substantiate the potential of UNAEN as a promising solution applicable in real-world clinical environments, with the capability to enhance diagnostic accuracy and facilitate image-guided therapies.
RaLiBEV: Radar and LiDAR BEV Fusion Learning for Anchor Box Free Object Detection Systems
Recent state-of-the-art works reveal that the fusion of radar and LiDAR can lead to robust detection in adverse weather.
GNN-PMB: A Simple but Effective Online 3D Multi-Object Tracker without Bells and Whistles
The global nearest neighbor (GNN) filter, as the earliest random vector-based Bayesian tracking framework, has been adopted in most of state-of-the-arts trackers in the automotive industry.
Unsupervised Representation Learning for 3D MRI Super Resolution with Degradation Adaptation
However, the difference in degradation representations between synthetic and authentic LR images suppresses the quality of SR images reconstructed from authentic LR images.
Contrastive Learning for Automotive mmWave Radar Detection Points Based Instance Segmentation
Deep learning-based instance segmentation enables real-time object identification from the radar detection points.
A Screen-Shooting Resilient Document Image Watermarking Scheme using Deep Neural Network
By applying this scheme, when the watermarked image is displayed on the screen and captured by a camera, the watermark can be still extracted from the captured photographs.
FMCNet: Feature-Level Modality Compensation for Visible-Infrared Person Re-Identification
Then, a feature-level modality compensation module is present to generate those missing modality-specific features from existing modality-shared ones.
3D High-Quality Magnetic Resonance Image Restoration in Clinics Using Deep Learning
We also analyzed several down-sampling strategies based on the acceleration factor, including multiple combinations of in-plane and through-plane down-sampling, and developed a controllable and quantifiable motion artifact generation method.
Deep Instance Segmentation with Automotive Radar Detection Points
Automotive radar provides reliable environmental perception in all-weather conditions with affordable cost, but it hardly supplies semantic and geometry information due to the sparsity of radar detection points.
Exploring Modality-shared Appearance Features and Modality-invariant Relation Features for Cross-modality Person Re-Identification
Most existing cross-modality person re-identification works rely on discriminative modality-shared features for reducing cross-modality variations and intra-modality variations.
Cross-Modality Person Re-identification
Person Re-Identification
How Distance Transform Maps Boost Segmentation CNNs: An Empirical Study
The \emph{second contribution} is that we systematically evaluated five benchmark methods on two representative public datasets.
