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Found 15 papers, 2 papers with code
EffNet: An Efficient Structure for Convolutional Neural Networks
With the ever increasing application of Convolutional Neural Networks to customer products the need emerges for models to efficiently run on embedded, mobile hardware.
Fast and Reliable Architecture Selection for Convolutional Neural Networks
The performance of a Convolutional Neural Network (CNN) depends on its hyperparameters, like the number of layers, kernel sizes, or the learning rate for example.
Aggregated Channels Network for Real-Time Pedestrian Detection
Convolutional neural networks (CNNs) have demonstrated their superiority in numerous computer vision tasks, yet their computational cost results prohibitive for many real-time applications such as pedestrian detection which is usually performed on low-consumption hardware.
A Statistical Defense Approach for Detecting Adversarial Examples
Then, the signature is projected onto the class-specific statistic vector to infer the input's nature.
FLIC: Fast Lidar Image Clustering
Lidar sensors are widely used in various applications, ranging from scientific fields over industrial use to integration in consumer products.
Fast Object Classification and Meaningful Data Representation of Segmented Lidar Instances
Object detection algorithms for Lidar data have seen numerous publications in recent years, reporting good results on dataset benchmarks oriented towards automotive requirements.
On the Robustness of Active Learning
Active Learning is concerned with the question of how to identify the most useful samples for a Machine Learning algorithm to be trained with.
Polynomial Trajectory Predictions for Improved Learning Performance
The rising demand for Active Safety systems in automotive applications stresses the need for a reliable short to mid-term trajectory prediction.
Context-Aware Scene Prediction Network (CASPNet)
Predicting the future motion of surrounding road users is a crucial and challenging task for autonomous driving (AD) and various advanced driver-assistance systems (ADAS).
What Can be Seen is What You Get: Structure Aware Point Cloud Augmentation
To train a well performing neural network for semantic segmentation, it is crucial to have a large dataset with available ground truth for the network to generalize on unseen data.
Fake it, Mix it, Segment it: Bridging the Domain Gap Between Lidar Sensors
We compare our method with two state of the art approaches for semantic lidar segmentation domain adaptation with a significant improvement for unsupervised and semi-supervised domain adaptation.
Semantic Segmentation of Radar Detections using Convolutions on Point Clouds
State-of-the-art algorithms for environment perception based on radar scans build up on deep neural network architectures that can be costly in terms of memory and computation.
Quantification of Uncertainties in Deep Learning-based Environment Perception
In this work, we introduce a novel Deep Learning-based method to perceive the environment of a vehicle based on radar scans while accounting for uncertainties in its predictions.
Deep Learning Method for Cell-Wise Object Tracking, Velocity Estimation and Projection of Sensor Data over Time
These approaches derive scene dynamics implicitly by correlating novel input and memorized data utilizing ConvNets.
CASPNet++: Joint Multi-Agent Motion Prediction
In this work, we focus on further enhancing the interaction modeling and scene understanding to support the joint prediction of all road users in a scene using spatiotemporal grids to model future occupancy.
