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Found 22 papers, 7 papers with code
SGV3D:Towards Scenario Generalization for Vision-based Roadside 3D Object Detection
Our method surpasses all previous methods by a significant margin in new scenes, including +42. 57% for vehicle, +5. 87% for pedestrian, and +14. 89% for cyclist compared to BEVHeight on the DAIR-V2X-I heterologous benchmark.
Dual Radar: A Multi-modal Dataset with Dual 4D Radar for Autonomous Driving
Compared with commonly used 3D radars, the latest 4D radars have precise vertical resolution and higher point cloud density, making it a highly promising sensor for autonomous driving in complex environmental perception.
SkipcrossNets: Adaptive Skip-cross Fusion for Road Detection
Multi-modal fusion is increasingly being used for autonomous driving tasks, as images from different modalities provide unique information for feature extraction.
Informative Data Selection with Uncertainty for Multi-modal Object Detection
To quantify the correlation in multi-modal information, we model the uncertainty, as the inverse of data information, in different modalities and embed it in the bounding box generation.
Federated Recommendation with Additive Personalization
To address these challenges, we propose Federated Recommendation with Additive Personalization (FedRAP), which learns a global view of items via FL and a personalized view locally on each user.
CAMO-MOT: Combined Appearance-Motion Optimization for 3D Multi-Object Tracking with Camera-LiDAR Fusion
As such, we propose a novel camera-LiDAR fusion 3D MOT framework based on the Combined Appearance-Motion Optimization (CAMO-MOT), which uses both camera and LiDAR data and significantly reduces tracking failures caused by occlusion and false detection.
A Benchmark for Automatic Medical Consultation System: Frameworks, Tasks and Datasets
In recent years, interest has arisen in using machine learning to improve the efficiency of automatic medical consultation and enhance patient experience.
A Machine Learning Method for Material Property Prediction: Example Polymer Compatibility
We present a brand-new and general machine learning method for material property prediction.
Incomplete Multi-View Weak-Label Learning with Noisy Features and Imbalanced Labels
A variety of modern applications exhibit multi-view multi-label learning, where each sample has multi-view features, and multiple labels are correlated via common views.
IPS300+: a Challenging Multimodal Dataset for Intersection Perception System
Through an IPS (Intersection Perception System) installed at the diagonal of the intersection, this paper proposes a high-quality multimodal dataset for the intersection perception task.
A novel multimodal fusion network based on a joint coding model for lane line segmentation
There has recently been growing interest in utilizing multimodal sensors to achieve robust lane line segmentation.
High Precision Medicine Bottles Vision Online Inspection System and Classification Based on Multi-Features and Ensemble Learning via Independence Test
To address the problem of online automatic inspection of drug liquid bottles in production line, an implantable visual inspection system is designed and the ensemble learning algorithm for detection is proposed based on multi-features fusion.
Adaptive Unimodal Cost Volume Filtering for Deep Stereo Matching
However, disparity is just a byproduct of a matching process modeled by cost volume, while indirectly learning cost volume driven by disparity regression is prone to overfitting since the cost volume is under constrained.
Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors
In this paper, we propose a deep learning based cloud detection method named multi-scale convolutional feature fusion (MSCFF) for remote sensing images of different sensors.
DenseASPP for Semantic Segmentation in Street Scenes
To this end, we propose Densely connected Atrous Spatial Pyramid Pooling (DenseASPP), which connects a set of atrous convolutional layers in a dense way, such that it generates multi-scale features that not only cover a larger scale range, but also cover that scale range densely, without significantly increasing the model size.
Ranked #5 on
Semantic Segmentation
on SkyScapes-Dense
Correction of "Cloud Removal By Fusing Multi-Source and Multi-Temporal Images"
Remote sensing images often suffer from cloud cover.
Locality-Sensitive Deconvolution Networks With Gated Fusion for RGB-D Indoor Semantic Segmentation
This layer can learn to adjust the contributions of RGB and depth over each pixel for high-performance object recognition.
Ranked #79 on
Semantic Segmentation
on NYU Depth v2
Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery
In this paper, an automatic multi-feature combined (MFC) method is proposed for cloud and cloud shadow detection in GF-1 WFV imagery.
Joint Multiview Segmentation and Localization of RGB-D Images Using Depth-Induced Silhouette Consistency
In this paper, we propose an RGB-D camera localization approach which takes an effective geometry constraint, i. e. silhouette consistency, into consideration.
MeshStereo: A Global Stereo Model With Mesh Alignment Regularization for View Interpolation
We present a novel global stereo model designed for view interpolation.
Query Adaptive Similarity Measure for RGB-D Object Recognition
The reasons are in two-fold: (1) existing similarity measures are sensitive to object pose and scale changes, as well as intra-class variations; and (2) effectively fusing RGB and depth cues is still an open problem.
Efficient 2D-to-3D Correspondence Filtering for Scalable 3D Object Recognition
3D model-based object recognition has been a noticeable research trend in recent years.
