Search Results for author:
Found 11 papers, 2 papers with code
A Point-Based Approach to Efficient LiDAR Multi-Task Perception
Unlike other LiDAR-based multi-task architectures, our proposed PAttFormer does not require separate feature encoders for multiple task-specific point cloud representations, resulting in a network that is 3x smaller and 1. 4x faster while achieving competitive performance on the nuScenes and KITTI benchmarks for autonomous driving perception.
Classification robustness to common optical aberrations
Experiments on ImageNet show that for a variety of different pre-trained DNNs, the performance varies strongly compared to disk-shaped kernels, indicating the necessity of considering realistic image degradations.
ECS -- an Interactive Tool for Data Quality Assurance
With the increasing capabilities of machine learning systems and their potential use in safety-critical systems, ensuring high-quality data is becoming increasingly important.
QI2 -- an Interactive Tool for Data Quality Assurance
The importance of high data quality is increasing with the growing impact and distribution of ML systems and big data.
Windscreen Optical Quality for AI Algorithms: Refractive Power and MTF not Sufficient
Further, as the industry is moving towards the modulation transfer function (MTF) as an alternative, we mathematically show that this metric cannot be used on windscreens alone, but that the windscreen forms a novel optical system together with the optics of the camera system.
Self-Supervised Multi-Object Tracking For Autonomous Driving From Consistency Across Timescales
We hypothesize that this drawback results from formulating self-supervised objectives that are limited to single frames or frame pairs.
Self-Supervised Representation Learning from Temporal Ordering of Automated Driving Sequences
Self-supervised feature learning enables perception systems to benefit from the vast raw data recorded by vehicle fleets worldwide.
Change Detection for Local Explainability in Evolving Data Streams
In particular, we show that local attributions can become obsolete each time the predictive model is updated or concept drift alters the data generating distribution.
On Hyperbolic Embeddings in 2D Object Detection
Object detection, for the most part, has been formulated in the euclidean space, where euclidean or spherical geodesic distances measure the similarity of an image region to an object class prototype.
Contrastive Object Detection Using Knowledge Graph Embeddings
Object recognition for the most part has been approached as a one-hot problem that treats classes to be discrete and unrelated.
SLPC: a VRNN-based approach for stochastic lidar prediction and completion in autonomous driving
Our algorithm is able to address the limitations of previous video prediction frameworks when dealing with sparse data by spatially inpainting the depth maps in the upcoming frames.
