Italy

automated learning

corner case detection

epistemic uncertainty estimation

object detection

machine learning

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


ICPR 2020 MAIN CONFERENCE

State-of-the-art instance segmentation techniques currently provide a bounding box, class, mask, and scores for each instance. What they do not provide is an epistemic uncertainty estimate of these predictions. With our approach, we want to identify corner cases by considering the epistemic uncertainty. Corner cases are data/situations that are underrepresented or not covered in our data set. Our work is based on Mask R-CNN. We estimate the epistemic uncertainty by extending the architecture with Monte-Carlo dropout layers. By repeatedly executing the forward pass, we create a large number of predictions per instance. Afterward, we cluster the predictions of an instance based on the bounding box coordinates. It becomes possible to determine the epistemic position uncertainty for the bounding boxes and the classifier's epistemic class uncertainty. For the epistemic uncertainty regarding the bounding box position and the class assignment, we provide a criterion for detecting corner cases utilizing the model's epistemic uncertainty.

Towards Corner Case Detection by Modeling the Uncertainty of Instance Segmentation Networks

technical paper

Novelty based Driver Identification on RR Intervals from ECG Data

Defect Detection of Stainless Steel Plates Using Deep Learning Technology

BiLuNet: A Multi-path Network for Semantic Segmentation on X-ray Images

A Hybrid Machine Learning Approach for Energy Consumption Prediction in Additive Manufacturing

Multiregion multiscale image segmentation with anisotropic diffusion

A Novel Multi-Feature Skeleton Representation for 3D Action Recognition

Refined Semantic Segmentation Network of City Remote Sensing Image

Real-Time Thermal Face Identification System for Low Memory Vision Applications Using CNN

Blockchain-based iris authentication in order tosecure IoT access and digital money spending

DeepFakes Evolution: Analysis of Facial Regions and Fake Detection Performance

poster

Premium content

Towards Corner Case Detection by Modeling the Uncertainty of Instance Segmentation Networks

Next from ICPR 2020 MAIN CONFERENCE

Novelty based Driver Identification on RR Intervals from ECG Data

Similar lectures

Hybrid Cascade Point Search Network for High Precision