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Found 13 papers, 3 papers with code
Med-DANet V2: A Flexible Dynamic Architecture for Efficient Medical Volumetric Segmentation
As a pioneering work, a dynamic architecture network for medical volumetric segmentation (i. e. Med-DANet) has achieved a favorable accuracy and efficiency trade-off by dynamically selecting a suitable 2D candidate model from the pre-defined model bank for different slices.
DrivingDiffusion: Layout-Guided multi-view driving scene video generation with latent diffusion model
To alleviate the problem, we propose a spatial-temporal consistent diffusion framework DrivingDiffusion, to generate realistic multi-view videos controlled by 3D layout.
3D-U-SAM Network For Few-shot Tooth Segmentation in CBCT Images
Accurate representation of tooth position is extremely important in treatment.
CityTrack: Improving City-Scale Multi-Camera Multi-Target Tracking by Location-Aware Tracking and Box-Grained Matching
Targets in urban traffic scenes often undergo occlusion, illumination changes, and perspective changes, making it difficult to associate targets across different cameras accurately.
Multi-source adversarial transfer learning for ultrasound image segmentation with limited similarity
Transfer learning provides the possibility to solve this problem, but there are too many features in natural images that are not related to the target domain.
Multi-source adversarial transfer learning based on similar source domains with local features
In order to ensure that the data from the target domain in different sub-networks in the same batch is exactly the same, we designed a multi-source domain independent strategy to provide the possibility for later local feature fusion to complete the key features required.
Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes
Modern autonomous driving systems are typically divided into three main tasks: perception, prediction, and planning.
Ranked #1 on
Trajectory Planning
on nuScenes
ByteTrackV2: 2D and 3D Multi-Object Tracking by Associating Every Detection Box
We propose a hierarchical data association strategy to mine the true objects in low-score detection boxes, which alleviates the problems of object missing and fragmented trajectories.
Robust Multi-Object Tracking by Marginal Inference
To address the problem, we present an efficient approach to compute a marginal probability for each pair of objects in real time.
PlaneSDF-based Change Detection for Long-term Dense Mapping
Given point clouds of the source and target scenes, we propose a three-step PlaneSDF-based change detection approach: (1) PlaneSDF volumes are instantiated within each scene and registered across scenes using plane poses; 2D height maps and object maps are extracted per volume via height projection and connected component analysis.
ByteTrack: Multi-Object Tracking by Associating Every Detection Box
ByteTrack also achieves state-of-the-art performance on MOT20, HiEve and BDD100K tracking benchmarks.
Ranked #1 on
Multiple Object Tracking
on BDD100K val
VoxelTrack: Multi-Person 3D Human Pose Estimation and Tracking in the Wild
We estimate 3D poses from the voxel representation by predicting whether each voxel contains a particular body joint.
Ranked #7 on
3D Multi-Person Pose Estimation
on Campus
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
Formulating MOT as multi-task learning of object detection and re-ID in a single network is appealing since it allows joint optimization of the two tasks and enjoys high computation efficiency.
Ranked #1 on
Multi-Object Tracking
on 2DMOT15
(using extra training data)
