The 2nd workshop on Structuring and Understanding of Multimedia heritAge Contents @ ACM Multimedia 2020

October 6th, 2020 Daniela Lopez de Luise

SUMAC 2020 – The 2nd workshop on Structuring and Understanding of Multimedia heritAge Contents

It is next week and videos are online! Do not forget to register to follow the live keynotes and Q&A sessions!

(Virtual) In conjunction with ACM Multimedia 2020
12 October 2020, Seattle, United States

https://sumac2020.ec-lyon.fr
https://2020.acmmm.org  

*** Aims and scope

The objective of the second edition of this workshop is to present and discuss the latest and most significant trends in the analysis, structuring and understanding of multimedia contents dedicated to the valorization of heritage, with emphasis on the unlocking of and access to the big data of the past. We welcome research contributions related to the following (but not limited to) topics:

– Multimedia and cross-domain data interlinking and recommendation
– Dating and spatialization of historical data
– Mixed media data access and indexing
– Deep learning in adverse conditions (transfer learning, learning with side information, etc.)
– Multi-modal time series analysis, evolution modeling
– Multi-modal and multi-temporal data rendering
– HCI / Interfaces for large scale data sets
– Smart digitization of massive quantities of data
– Benchmarking, open data movement

*** Keynote speakers

– Andre Araujo (Google, US): Deep Image Features for Instance-level Recognition and Matching.
– Livio de Luca (CNRS, France): modeling, semantisation and restitution of 3D digital heritage objects – application to digital restoration of Notre-Dame de Paris.

*** Organization

This year, the workshop appears virtually as the main conference. To each oral paper is associated a pre-recorded video oral presentation (15 min), which has been made available on the website of the event since the 5th of October. Attendees are welcome to provide questions on the associated commentary section. A synthesis of your questions will be reported by the chairs to the authors, who should have time to respond to them during the live Q&A sessions on the 12th of October. Please note that priority will be given to attendees during these live time slots, so we encourage you to register in order to attend the live Q&A sessions. The introduction, wrap-up and two keynote sessions (followed by Q&A) will be running online as live meetings on the 12th of October.

*** Organizers

Valerie Gouet-Brunet (LaSTIG Lab / IGN – Gustave Eiffel University, France)
Margarita Khokhlova (LaSTIG/LIRIS Labs, IGN & Centrale Lyon, France)
Ronak Kosti (Pattern Recognition Lab / FAU Erlangen-Nürnberg, Germany)
Liming Chen (LIRIS Lab / Centrale Lyon, France)
Xu-Cheng Yin (University of Science and Technology Beijing, China) 

                                

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I-WANDER 2021: 1st International Workshop on Anomalies Detection and Road Traffic Analysis in Smart Cities

October 6th, 2020 Daniela Lopez de Luise
Dear Colleagues,

 

the main aim of Smart Cities is improving the daily life of the citizens and supporting their habits by proposing subject oriented services. In urban scenarios, this improvement is mainly oriented to pedestrians and drivers, which may take advantage from surveillance systems providing services and guaranteeing safe living environments. To this purpose, we kindly invite you to submit your work to the 1st International Workshop on Anomalies Detection and Road Traffic Analysis in Smart Cities: methods, applications cloud-based technologies – I-WANDER 2021, in conjuction with the the 10th International Conference on Pattern Recognition Applications and Methods – ICPRAM 2021. The main scope of this workshop is to gather papers and proposals dealing with methods and systems able to detect anomalous events in urban traffic scenarios and to launch alerts in cases their presence is confirmed. In fact, IoT, cloud and CV based approaches may serve as underlying systems for detecting several anomalous situations, both related to vehicles and to pedestrians. 
The main topics of interest of I-WANDER 2021 will include, but are not limited to:
  • Anomalies Detection in Urban Scenarios
  • Person and Object detection in lanes
  • Fight recognition
  • Gait analysis in uncontrolled scenarios
  • Dangerous pedestrian crossing
  • Background/Foreground segmentations in urban scenarios
  • Car and pedestrian tracking
  • Lane Detection/Segmentation
  • Urban Scenario Databases
  • Car re-identification
  • License Plate recognition
  • Object Trajectory estimation/prediction
  • Cloud based approaches for traffic management
  • IoT services in Smart Cities
  • Biometric re-identification in uncontrolled scenarios
IMPORTANT DATES
Paper Submission: November 26, 2020
Authors Notification: December 14, 2020
Camera Ready and Registration: December 22, 2020
PUBLICATIONS
After thorough reviewing by the workshop program committee, all accepted papers will be published in a special section of the conference proceedings book, and will be available at the SCITEPRESS Digital Library (http://www.scitepress.org/DigitalLibrary/).
SCITEPRESS is a member of CrossRef (http://www.crossref.org/) and every paper is given a DOI (Digital Object Identifier), and is indexed by Scopus. Moreover, the best papers accepted at I-WANDER 2021 will be selected to submit an extended version of their work at the Special Issue “Cloud-Based Biometrics for Smart Cities” of the international journal MDPI Smart Cities (ISSN 2624-6511), that will be fully indexed in Scopus starting from next year (2021). Invited works will also have a total or partial discount on the APC, for open access publication.
We would also appreciate it if you could spread this call for papers to your interested colleagues.
Yours sincerely,
the Workshop General Co-chairs
Silvio Barra (silvio.barra@unina.it)
Salvatore Carta (sebastianpodda@unica.it)
Sebastian Podda (salvatore@unica.it)
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CFP Special Issue on Computer Vision and Deep Learning for Remote Sensing Applications

October 6th, 2020 Daniela Lopez de Luise

CALL FOR PAPERS

 

* Special Issue on Computer Vision and Deep Learning for Remote Sensing Applications *

Journal: Remote Sensing (MDPI), IF 4.118

 

Guest Editors: Hyungtae Lee, Sungmin Eum, Claudio Piciarelli

 

Full info: http://mdpi.com/si/46402

 

Deadline: accepted papers will be published continuously (as soon as accepted) till the deadline (31 March 2021)

===================================================================

 

Abstract:

 

Today, the field of computer vision and deep learning is rapidly progressing into many applications, including remote sensing, due to its remarkable performance. Especially for remote sensing, a myriad of challenges due to difficult data acquisition and annotation have not been fully solved yet. The remote sensing community is waiting for a breakthrough to address these challenges by utilizing high-performance deep learning-based models that typically require large-scale annotated datasets.

 

This issue is looking for such breakthroughs focusing on the advances in remote sensing using computer vision, deep learning and artificial intelligence. Although broad in scope, contributions with a specific focus are expected.

 

For this special issue, we welcome the most recent advancements related, but not limited to:

 

* Deep learning architecture for remote sensing

* Machine learning for remote sensing

* Computer vision method for remote sensing

* Classification / Detection / Regression

* Unsupervised feature learning for remote sensing

* Domain adaptation and transfer learning with computer vision and deep learning for remote sensing

* Anomaly/novelty detection for remote sensing

* New dataset and task for remote sensing

* Remote sensing data analysis

* New remote sensing application

* Synthetic remote sensing data generation

* Real-time remote sensing

* Deep learning-based image registration

 

Dr. Hyungtae Lee

Dr. Sungmin Eum

Dr. Claudio Piciarelli

Guest Editors

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ICPR2020 Workshop: Multi-Modal Deep Learning: Challenges and Applications

October 6th, 2020 Daniela Lopez de Luise
Call for papers  ICPR’2020 Workshop: Multi-Modal Deep Learning: Challenges and Applications


In conjunction with the 25th International Conference on Pattern Recognition (ICPR 2020),  January 10-15, 2021 (https://www.micc.unifi.it/icpr2020/)


INFORMATION ON MMDLCA
Deep learning is now recognized as one of the key software engines that drives the new industrial revolution. The majority of current deep learning research efforts have been dedicated to single-modal data processing. Pronounced manifestations are deep learning based visual recognition and speech recognition. Although significant progress made, single-modal data is often insufficient to derive accurate and robust deep models in many applications. Our digital world is by nature multi-modal, that combines different modalities of data such as text, audio, images, animations, videos and interactive content. Multi-modal is the most popular form for information representation and delivery. For example, posts for hot social events are typically composed of textual descriptions, images and videos. For medical diagnosis, the joint use of medical imaging and textual reports is also essential. Multi-modal data is common for human to make accurate perceptions and decisions. Multi-modal deep learning that is capable of learning from information presented in multiple modalities and consequently making predictions based on multi-modal input is much in demand.


This workshop calls for scientific works that illustrate the most recent progress on multi-modal deep learning. In particular, multi-modal data capture, integration, modelling, understanding and analysis, and how to leverage them to derive accurate and robust AI models in many applications. It is a timely topic following the rapid development of deep learning technologies and their remarkable applications to many fields. It will serve as a forum to bring together active researchers and practitioners to share their recent advances in this exciting area. In particular, we solicit original and high-quality contributions in: (1) presenting state-of-the-art theories and novel application scenarios related to multi-modal deep learning; (2) surveying the recent progress in this area; and (3) developing benchmark datasets and evaluations. We welcome contributions coming from various communities (i.e., visual computing, machine learning, multimedia analysis, distributed and cloud computing, etc.) to submit their novel results.


TOPICS
The list of topics includes, but not limited to:
• Multi-modal intelligent data acquisition and management
• Multi-modal benchmark datasets and evaluations
• Multi-modal representation learning and applications
• Multi-modal data driven visual analysis and understanding
• Multi-modal object detection, classification, recognition and segmentation
• Multi-modal information tracking, retrieval and identification
• Multi-modal social event analysis
• Multi-modal medical diagnosis
• Multi-modal machine learning from incomplete data
• Deep neural network architectures for multi-modal data processing
• Multi-modal big data analytics
• Emerging multi-modal deep learning applications


SUBMISSION GUIDELINES
Submissions must be formatted in accordance with the Springer's Computer Science Proceedings guidelines (https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines). Two types of contribution will be considered:
• Full papers (10-12 pages, including references)
• Short papers (6-8 pages, including references)
Accepted manuscripts will be included in the ICPR 2020 Workshop Proceedings Springer volume. Once accepted, at least one author is expected to attend the event and orally present the paper. The submission platform will be available soon.


IMPORTANT DATES
• Workshop submission deadline: Oct. 15th, 2020
• Workshop author notification: Nov. 10th, 2020
• Camera-ready submission: Nov. 15th, 2020
• Finalized workshop program: Dec. 1st, 2020
• Workshop day: Jan. 11, 2021


CONTACTS
For any inquiry you may have, please send an email to:
Zhineng Chen at zhineng.chen@ia.ac.cn,
Xirong Li at xirong@ruc.edu.cn,
Efstratios Gavves at e.gavves@uva.nl
Mei Chen at may4mc@gmail.com
Ioannis (Yiannis) Kompatsiaris at ikom@iti.gr
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DeepLearn 2021 Winter: early registration October 8

October 6th, 2020 Daniela Lopez de Luise
 
4th INTERNATIONAL SCHOOL ON DEEP LEARNING
 
DeepLearn 2021 Winter
 
Milan, Italy
 
January 11-15, 2021
 
Co-organized by:
 
Department of Information Engineering
Marche Polytechnic University
 
Institute for Research Development, Training and Advice – IRDTA
Brussels/London
 
https://irdta.eu/deeplearn2021w/
 
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