Call for papers Manifold Learning from Euclid to Riemann ICPR Workshop

“> https://sites.google.com/view/manlearn2020/

We are organizing the Manifold Learning from Euclid to Riemann Workshop In Conjunction with the 25th International Conference On Pattern Recognition, Milan, Italy 10 – 15 January 2021

We encourage discussions on recent advances, ongoing developments, and novel applications of manifold learning, optimization, feature representations and deep learning techniques. We are soliciting original contributions that address a wide range of theoretical and practical issues including, but not limited to:

Theoretical Advances related to manifold learning such as

·  Dimensionality Reduction (e.g., Locally Linear Embedding, Laplacian Eigenmaps and etc.)

·  Clustering (e.g., discriminative clustering)

·  Kernel methods

·  Metric Learning

·  Time series on non-linear manifolds

·  Transfert learning on non-linear manifolds

·  Generative Models on non-linear manifolds

·  Subspace Methods (e.g., Subspace clustering)

·  Advanced Optimization Techniques (constrained and non-convex optimization techniques on non-linear manifolds)

·  Mathematical Models for learning sequences

·  Mathematical Models for learning Shapes

·  Deep learning and non-linear manifolds

·  Low-rank factorization methods

Applications: 

·  Biometrics

·  Image/video recognition

·  Action/activity recognition

·  Facial expressions recognition

·  Learning and scene understanding

·  Medical imaging

·  Robotics

·  Other related topics not listed above

Special Issue: We will also invite selected papers for submission to a special issue on
Learning with Manifolds  in computer vision in IMAGE AND VISION COMPUTING Journal. https://www.journals.elsevier.com/image-and-vision-computing/call-for-papers/special-issue-on-learning-with-manifolds-in-computer-vision

Important Dates

  • Workshop submission deadline: October 10th

  • Workshop author notification: November 10th

  • Camera-ready submission: November 15th

  • Finalized workshop program: December 1st

DeepLearn 2021 Winter: early registration September 14

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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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CFP: SI Combinatorial Optimization in Imaging Sciences

We would like to invite you to submit a paper to the  Special Issue of the Journal of Combinatorial Optimization (Springer) devoted to Combinatorial Optimization in Imaging Sciences.

The topics of interest include (but are not limited to):

  • Optimization problems in discrete geometry for computer imagery
  • Combinatorial optimization problems on digital manifolds
  • Integer programming models and solutions
  • Graph formulations and algorithms for imaging problems
  • Extremal combinatorial properties of digitized sets
  • Combinatorial optimization for topological image analysis
  • Combinatorial algorithms for processing very large digital images
  • Compressed image representation and parallel algorithms for processing massive data sets
  • Artificial intelligence and optimization
  • Neural networks and machine learning for image analysis and processing.
The submission deadline is October 15, 2020. The accepted papers are expected to appear in May 2021.
If you have any questions, please direct them to the Guest Editors Reneta Barneva (barneva@fredonia.edu) and Tibor Lukic (tibor@uns.ac.rs)

International Workshop on Pattern Forecasting — CfP due Oct 10th 2020

May I draw your attention on an upcoming workshop on Pattern Forecasting (PATCAST), to gather scientific contributions on forecasting, both regarding the general theory and its manifold applications across the various fields of science (computer vision, NLP, machine learning, robotics, finance, environmental sciences, bioinformatics etc.)

Paper contributions are due on October 10th, 2020
Please consider submitting and participating.
And please spread the news.
The workshop would be at ICPR, on January 11th 2021.

More details in the CfP below and at: https://sites.google.com/di.uniroma1.it/patcast

Best regards,
Fabio

(We apologize if you receive multiple copies of this message.)

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PATCAST 2020
International Workshop on Pattern Forecasting
https://sites.google.com/di.uniroma1.it/patcast
January 11th, 2021, Milan, Italy
in association with ICPR 2020
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>> PAPER submission:         October 10th, 2020<<
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CALL FOR PAPERS
Anticipating patterns has become a crucial activity in the last years, due to the combined availability of huge amount of data, techniques for exploiting noisy information, transferring knowledge across domains, and the need of forecasting services within many heterogeneous domains, from computer science to environmental sciences, from economics to robotics and from bioinformatics to social sciences and humanities. A growing spectrum of applications in self-driving cars, weather forecasting, financial market prediction, real-time epidemic forecasting, and social network modeling needs to be explored within a same venue. This workshop aims therefore to identify commonalities, gather lessons learnt across domains, discuss modern and most successful techniques, and foster the exchange of new ideas, which may extend to other novel fields too.

The International Workshop on Pattern Forecasting addresses the general problem of forecasting patterns. This is not just limited to a specific domain, but rather intended as cross-fertilization of different disciplines. By doing so, it seeks to highlight possible general-purpose approaches which may be applied to a large span of data types, promoting and motivating further studies in specific directions. As an example, techniques for predicting the diffusion of epidemics are currently adopted to forecasting activities within social networks. We are convinced that many other hybridations are ready to be explored.

We plan a number of invited talks by senior scientists from different domains, an extensive poster session for stimulating collaborations among young researchers, and a final panel to gather the major challenges emerged in the day and the effective techniques. We anticipate fructuous discussion on the relation between techniques and challenges and on the adoption of techniques beyond those fields where they have been originally designed.

SCOPE
The workshop seeks contributions from researchers and practitioners from different domains, to share current best algorithms and practices, to foster discussion among diverse communities and to define common grounds for joint progress, within the general artificial intelligence and pattern recognition.

The topics of interest for the convention include, but are not limited to, the following areas:
– Forecasting in computer science, environmental sciences, economics, robotics, bioinformatics, social sciences, humanities
– Short term/long term prediction
– Structured input/structured output forecasting
– Distributed (cloud) forecasting
– Real-time forecasting
– Hierarchical forecasting
– Judgemental forecasting
– Integration of system dynamics and forecasting models
– Performance measurement
– Knowledge sharing and organisational learning
– Forecasting visual patterns/styles
– Pedestrian/vehicle trajectory forecasting
– Forecasting for Industry 4.0
– Predictive maintenance
– Weather forecasting
– Earthquakes/eruption forecasting
– Econometric Forecasting
– Financial Forecasting and Risk Analysis
– Forecasting and Planning Systems
– Forecasting Electricity Load and Prices
– Forecasting for Workforce Management
– Forecasting Support Systems (FSS)
– Intermittent Demand Forecasting (Forecasting of Count Series)
– Robot planning
– Intention prediction
– forecasting for genomics
– Virality/trend prediction into social networks
– Forecasting as product recommendation

IMPORTANT DATES
– Workshop date: January 11th
– Paper submission deadline: October 10st
– Paper author notification:  November 10th
– Camera-ready submission: November 15th
– Finalized workshop program: December 1st

INVITED SPEAKERS
– Pratik Prabhanjan Brahma, Volkswagen, Belmont, CA
– Thomas Brox, University of Freiburg, DE
– Carolina García Martos, Universidad Politécnica de Madrid, ES
– Marco Pavone, Stanford University, CA
– Giovanni Maria Farinella, University of Catania, IT
– Marco Bee, University of Trento, IT
– Dino Zardi, University of Trento, IT
– Novella Bartolini, Sapienza University, IT

ORGANIZATION
Workshop Organizers:
– Marco Cristani, University of Verona, marco.cristani@univr.it
– Kris Kitani, Carnegie Mellon University, kkitani@cs.cmu.edu
– Fabio Galasso, Sapienza University, galasso@di.uniroma1.it
– Siyu Tang, ETH Zürich, siyu.tang@tuebingen.mpg.de

PROGRAM COMMITTEE
Further to the organizers, a panel of external reviewers would be employed in the program committee, including:
– Sikandar Amin (OSRAM)
– Bharti Munjal (TUM)
– Nick Rhinehart (UC Berkeley)
– Wei-Chiu Ma (MIT)
– De-An Huang (Stanford)
– Namhoo Lee (Oxford)
– Ye Yuan (CMU)
– Yan Zhang (MPI Intelligent Systems)
– Miao Liu (Georgia Tech)
– Qiuhong Ke (University of Melbourne)
– Giorgio Roffo (University of Glasgow)
– Francesco Setti (University of Verona)

SUBMISSION INSTRUCTIONS
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) as full paper (12-15 pages). 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 present the paper. Papers will be presented as posters, since orals will be dedicated to invited talks.
Submission would be done via Microsoft CMT3:
https://cmt3.research.microsoft.com/PATCAST2021

RESOURCES
Webpage:
https://sites.google.com/di.uniroma1.it/patcast

Prof Fabio Galasso
Sapienza University

+39 345 5969678
https://fgalasso.bitbucket.io/

CFP: Image and Vision Computing,”Deadline extension to Nov. 2 , 2020″

Image and Vision Computing

CALL FOR PAPERS

Special Issue on Advances in Domain Adaptation for Computer Vision

 

Aim and Scope: 

In daily routines, humans, not only learn and apply knowledge for visual tasks but also have intrinsic abilities to transfer knowledge between related vision tasks. For example, if a new vision task is relevant to any previous learning, it is possible to transfer the learned knowledge for handling the new vision task. In developing new computer vision algorithms, it is desired to utilize these capabilities to make the algorithms adaptable. Generally, traditional computer vision methods do not adapt to a new task and have to learn the new task from the beginning. These methods do not consider that the two visual tasks may be related and the knowledge gained in one may be applied to learn the other one efficiently in lesser time. Domain adaptation for computer vision is the area of research, which attempts to mimic this human behavior by transferring the knowledge learned in one or more source domains and use it for learning the related visual processing task in the target domain. Recent advances in domain adaptation, particularly in cotraining, transfer learning, and online learning have benefited computer vision research significantly. For example, learning from high-resolution source domain images and transferring the knowledge to learning low-resolution target domain information. This special issue will focus on the recent advances in domain adaptation for different computer vision tasks. 


 Topics of interest include, but are not limited to: 

·       Domain adaptation for machine learning frameworks for learning deep representations 

·       Domain adaptation for face detection/recognition and tracking

·       Domain adaptation for object detection/ recognition and tracking

·       Domain adaptation and hybrid models for real-time computer vision tasks

·       Domain adaptation for human pose detection/recognition and estimation 

·       Domain adaptation for event/action detection and recognition

·       Domain adaptation for few-shot learning

·       Domain adaptation for deep neural network optimization


Important Dates: 

Paper submission due: November 2, 2020. 

Final decision: March 31, 2021. 


Paper evaluation and submission: 

Submitted papers should present original, unpublished work, relevant to one of the topics of the Special Issue. All submitted papers will be evaluated on the basis of relevance, the significance of contribution, technical quality, and quality of presentation, by at least two independent reviewers (the papers will be reviewed following standard peer-review procedures of the Journal). Each paper will be reviewed rigorously and possibly in two rounds. Prospective authors should follow the formatting and Instructions of Image and Vision Computing at https://www.elsevier.com/journals/image-and-vision-computing/0262-8856/guide-for-authors, and invited to submit their papers directly via the online submission system at https://www.editorialmanager.com/IMAVIS/default.aspx. When submitting your manuscript please select the article type “VSI: Advances in Domain Adaptation for Computer Vision (ADACV)” Please submit your manuscript before the submission deadline. https://www.journals.elsevier.com/image-and-vision-computing/call-for-papers/advances-in-domain-adaptation-for-computer-vision 


Guest Editors:

Dr. Pourya Shamsolmoali 

Institute of Image Processing & Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China. 

Email: pshams@sjtu.edu.cn 


Prof. Salvador Garcaí 

Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain. 

Email: salvagl@decsai.ugr.es 


Prof. Huiyu Zhou 

Department of Informatics, University of Leicester, Leicester, UK. 

Email: hz143@leicester.ac.uk 


Prof. M. Emre Celebi 

Department of Computer Science, University of Central Arkansas, Conway, Arkansas, USA. 

Email: ecelebi@uca.edu 

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