Italy

video annotation

instance detection

tracking

segmentation

semi-supervised

labeling

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

Training machine learning models in a supervised manner
requires vast amounts of labeled data, where the labels are typically
provided by humans manually annotating samples using a variety of
tools. In this work, an intelligent annotation tool is proposed to combine
the fast and efficient labeling capabilities of modern machine learning
models with the reliable and accurate but slow correction capabilities of
human annotators. For the example of instance segmentation in videos,
techniques to interactively condition a model on previously predicted
and manually annotated (corrected) frames are explored to create an
iterative workflow combining the advantages of the intelligent model and
the human annotator. Thereby, the intelligent model conducts the bulk
of the work, performing instance detection, tracking, and segmentation,
and enables the human annotator to correct individual frames selectively
and instances. The proposed approach is based on the one-shot learning
paradigm, and, thus, avoids the computational cost of online retraining.
For this purpose, we use Siamese neural networks to transfer annotations
from one video frame to another. Multiple interaction options regarding
the choice of the additional input data to the neural network, e.g., model
predictions or manual corrections, are explored to refine the labeling
performance of the given model and speed up the annotation process.

Intelligent and Interactive Video Annotation for Instance Segmentation using Siamese Neural Networks

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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

poster

Premium content

Intelligent and Interactive Video Annotation for Instance Segmentation using Siamese Neural Networks

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