Italy

ICPR 2020

Text-Fcos method

technical paper

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. 


Hybrid Loss with Network Trimming for Disease Recognition in Gastrointestinal Endoscopy

DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation

Efficient Detection of Lesions during Endoscopy

Introduction to the ICPR 2020 Competition on Text Block Segmentation on a NewsEye Dataset

Opening Remarks

Presentation from the winner teams 2

Presentation from the winner teams 3

Solution for Product-10k Challenge:Large-scale Product Recognition

Bag of freebies for fine-grained product recognition

EffDB-UNet method

https://d3smihljt9218e.cloudfront.net/transcript/9603/subtitle/409c3e230659ba7b097b2995dd5c09ab.vtt

English (automatic)

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Text-Fcos method

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Hybrid Loss with Network Trimming for Disease Recognition in Gastrointestinal Endoscopy

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