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Found 12 papers, 6 papers with code
WildOcc: A Benchmark for Off-Road 3D Semantic Occupancy Prediction
3D semantic occupancy prediction is an essential part of autonomous driving, focusing on capturing the geometric details of scenes.
Proto-OOD: Enhancing OOD Object Detection with Prototype Feature Similarity
The limited training samples for object detectors commonly result in low accuracy out-of-distribution (OOD) object detection.
TeFF: Tracking-enhanced Forgetting-free Few-shot 3D LiDAR Semantic Segmentation
However, this approach introduces a data imbalance biased to novel data that presents a new challenge of catastrophic forgetting.
PID: Physics-Informed Diffusion Model for Infrared Image Generation
Infrared imaging technology has gained significant attention for its reliable sensing ability in low visibility conditions, prompting many studies to convert the abundant RGB images to infrared images.
PA&DA: Jointly Sampling PAth and DAta for Consistent NAS
Our method only requires negligible computation cost for optimizing the sampling distributions of path and data, but achieves lower gradient variance during supernet training and better generalization performance for the supernet, resulting in a more consistent NAS.
Few-shot 3D LiDAR Semantic Segmentation for Autonomous Driving
Thus, we propose a few-shot 3D LiDAR semantic segmentation method that predicts both novel classes and base classes simultaneously.
Autonomous Driving
Generalized Few-Shot Semantic Segmentation
+4
Unleashing the Power of Gradient Signal-to-Noise Ratio for Zero-Shot NAS
Neural Architecture Search (NAS) aims to automatically find optimal neural network architectures in an efficient way.
AGNAS: Attention-Guided Micro- and Macro-Architecture Search
We utilize the attention weights to represent the importance of the relevant operations for the micro search or the importance of the relevant blocks for the macro search.
DU-DARTS: Decreasing the Uncertainty of Differentiable Architecture Search
Differentiable Neural Architecture Search (DARTS) recently attracts a lot of research attention because of its high efficiency.
Ranked #9 on
Neural Architecture Search
on CIFAR-100
SemanticPOSS: A Point Cloud Dataset with Large Quantity of Dynamic Instances
3D semantic segmentation is one of the key tasks for autonomous driving system.
Incorporating Human Domain Knowledge in 3D LiDAR-based Semantic Segmentation
We propose a new method that makes full use of the advantages of traditional methods and deep learning methods via incorporating human domain knowledge into the neural network model to reduce the demand for large numbers of manual annotations and improve the training efficiency.
Semantic Segmentation of 3D LiDAR Data in Dynamic Scene Using Semi-supervised Learning
This work studies the semantic segmentation of 3D LiDAR data in dynamic scenes for autonomous driving applications.
Robotics
