↓ Skip to main content
What is this page? Embed badge

Deep Neural Networks and Data for Automated Driving

Overview of attention for book
Cover of 'Deep Neural Networks and Data for Automated Driving'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety
  3. Altmetric Badge
    Chapter 2 Does Redundancy in AI Perception Systems Help to Test for Super-Human Automated Driving Performance?
  4. Altmetric Badge
    Chapter 3 Analysis and Comparison of Datasets by Leveraging Data Distributions in Latent Spaces
  5. Altmetric Badge
    Chapter 4 Optimized Data Synthesis for DNN Training and Validation by Sensor Artifact Simulation
  6. Altmetric Badge
    Chapter 5 Improved DNN Robustness by Multi-task Training with an Auxiliary Self-Supervised Task
  7. Altmetric Badge
    Chapter 6 Improving Transferability of Generated Universal Adversarial Perturbations for Image Classification and Segmentation
  8. Altmetric Badge
    Chapter 7 Invertible Neural Networks for Understanding Semantics of Invariances of CNN Representations
  9. Altmetric Badge
    Chapter 8 Confidence Calibration for Object Detection and Segmentation
  10. Altmetric Badge
    Chapter 9 Uncertainty Quantification for Object Detection: Output- and Gradient-Based Approaches
  11. Altmetric Badge
    Chapter 10 Detecting and Learning the Unknown in Semantic Segmentation
  12. Altmetric Badge
    Chapter 11 Evaluating Mixture-of-Experts Architectures for Network Aggregation
  13. Altmetric Badge
    Chapter 12 Safety Assurance of Machine Learning for Perception Functions
  14. Altmetric Badge
    Chapter 13 A Variational Deep Synthesis Approach for Perception Validation
  15. Altmetric Badge
    Chapter 14 The Good and the Bad: Using Neuron Coverage as a DNN Validation Technique
  16. Altmetric Badge
    Chapter 15 Joint Optimization for DNN Model Compression and Corruption Robustness
Attention for Chapter 8: Confidence Calibration for Object Detection and Segmentation
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (58th percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

twitter
6 X users

Citations

dimensions_citation
25 Dimensions

Readers on

mendeley
12 Mendeley
Summary X Dimensions citations
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Chapter title
Confidence Calibration for Object Detection and Segmentation
Chapter number 8
Book title
Deep Neural Networks and Data for Automated Driving
Published in
arXiv, June 2022
DOI 10.1007/978-3-031-01233-4_8
Book ISBNs
978-3-03-101232-7, 978-3-03-101233-4
Authors

Fabian Küppers, Anselm Haselhoff, Jan Kronenberger, Jonas Schneider, Küppers, Fabian, Haselhoff, Anselm, Kronenberger, Jan, Schneider, Jonas

View on publisher site
X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 25%
Student > Ph. D. Student 1 8%
Other 1 8%
Student > Bachelor 1 8%
Unknown 6 50%
Readers by discipline Count As %
Computer Science 4 33%
Engineering 2 17%
Unknown 6 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 June 2022.
All research outputs
#13,798,575
of 24,093,053 outputs
Outputs from arXiv
#206,161
of 1,020,419 outputs
Outputs of similar age
#173,496
of 428,783 outputs
Outputs of similar age from arXiv
#6,471
of 36,375 outputs
Altmetric has tracked 24,093,053 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,020,419 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done well, scoring higher than 78% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 428,783 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 58% of its contemporaries.
We're also able to compare this research output to 36,375 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.

This page is provided by Altmetric.