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Found 10 papers, 2 papers with code
Towards learning-based planning:The nuPlan benchmark for real-world autonomous driving
Beyond the dataset, we provide a simulation and evaluation framework that enables a planner's actions to be simulated in closed-loop to account for interactions with other traffic participants.
BOTT: Box Only Transformer Tracker for 3D Object Tracking
The similarity between these learned embeddings can be used to link the boxes of the same object.
MotionTrack: End-to-End Transformer-based Multi-Object Tracing with LiDAR-Camera Fusion
Multiple Object Tracking (MOT) is crucial to autonomous vehicle perception.
The efficacy of Neural Planning Metrics: A meta-analysis of PKL on nuScenes
A high-performing object detection system plays a crucial role in autonomous driving (AD).
Attentive Weights Generation for Few Shot Learning via Information Maximization
Generating the classification weights has been applied in many meta-learning methods for few shot image classification due to its simplicity and effectiveness.
Few-Shot Regression via Learning Sparsifying Basis Functions
Recent few-shot learning algorithms have enabled models to quickly adapt to new tasks based on only a few training samples.
Efficient and Deep Person Re-Identification using Multi-Level Similarity
In this work, we propose an efficient, end-to-end fully convolutional Siamese network that computes the similarities at multiple levels.
Non-Rigid Object Tracking via Deformable Patches Using Shape-Preserved KCF and Level Sets
Part-based trackers are effective in exploiting local details of the target object for robust tracking.
Deep neural networks on graph signals for brain imaging analysis
In particular, our work proposes a new deep neural network design that integrates graph information such as brain connectivity with fully-connected layers.
