Deadline Reminder: ICML 2021 workshop on Subset Selection in ML

June 1st, 2021 Daniela Lopez de Luise
ICML 2021 Workshop on Subset Selection in ML  

 

Website: https://sites.google.com/view/icml-2021-subsetml/home. 

Important Dates:

§ Submission deadline: Sunday, June 6th, 23:59 AOE 

§ Author notification: Wednesday, June 16th

§ Camera-ready deadline and videos for selected talks: June 25th

§ Workshop date: Saturday, 24th July 2021

We will be using CMT to handle paper submissions (https://cmt3.research.microsoft.com/SUBSETML2021). Please submit papers before the deadline above.   

 Submissions in the form of extended abstracts must be at most 6 pages long (not including references and an unlimited number of pages for supplemental material, which reviewers are not required to take into account) and adhere to the ICML format. You can submit your NeurIPS 2021 papers (under review).  

 

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2nd ACM Multimedia Grand Challenge on Deep Video Understanding (Oct. 20 – 24, 2021)

June 1st, 2021 Daniela Lopez de Luise
Deep video understanding is a difficult task which requires systems to develop a deep analysis and understanding of the relationships between different entities in video, to use known information to reason about other, more hidden information, and to populate a knowledge graph (KG) with all acquired information. To work on this task, a system should take into consideration all available modalities (speech, image/video, and in some cases text). The aim of this new challenge is to push the limits of multimodal extraction, fusion, and analysis techniques to address the problem of analyzing long duration videos holistically and extracting useful knowledge to utilize it in solving different types of queries. The target knowledge includes both visual and non-visual elements. As videos and multimedia data are getting more and more popular and usable by users in different domains, the research, approaches and techniques we aim to apply in this Grand Challenge will be very relevant in the coming years and near future.
Challenge Overview:
Interested participants are invited to apply their approaches and methods on an extended novel Deep Video Understanding (DVU) dataset being made available by the challenge organizers.  The dataset will be annotated by human assessors and final ground truth, both at the overall movie level (Ontology of relations, entities, actions & events, Knowledge Graph, and names and images of all main characters), and the individual scene level (Ontology of locations, people/entities, attributes for these and interactions between) will be provided for 50% of the dataset to participating researchers for training and development of their systems. The organizers will support evaluation and scoring for a hybrid of main query types, at the overall movie level and at the individual scene level distributed with the dataset (please refer to the dataset webpage for more details):

Example Question types at Overall Movie Level:

1- Multiple choice question answering on part of Knowledge Graph for selected movies.
2- Possible path analysis between persons / entities of interest in a Knowledge Graph extracted from selected movies.
3- Fill in the Graph Space – Given a partial graph, systems will be asked to fill in the graph space.

Example Question types at Individual Scene Level:

1- Find the next or previous interaction, given two people, a specific scene, and the interaction between them.

2- Classify scene sentiment from a given scene.
3- Fill in the Graph Space – Given a partial graph for a scene, systems will be asked to fill in the graph space.
4- Match between selected scenes and set of scene descriptions written in natural language

Challenge Website:
https://sites.google.com/view/dvuchallenge2021/home/

Important Dates:

Complete HLVU annotations for development and testing data ,used in 2020, available: drive.google.com/drive/u/0/folders/1q1Ca0aFJrF9tB8hsw-mrI9d4tzy5wlPZ

DVU development data release: Available now from: https://www-nlpir.nist.gov/projects/trecvid/dvu/training/
Testing dataset release :  https://www-nlpir.nist.gov/projects/trecvid/dvu/testing
Testing queries release : June 6, 2021
Run submissions due to organizers: July 11, 2021
Paper submission deadline: July 11, 2021
Results released back to participants: TBD
Notification to authors: TBD
camera-ready submission: TBD
ACM Multimedia dates: October 20 – 24, 2021

Thank You!
Tha DVU2021 Organizers


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ICCV 2021 – Deep Learning for Geometric Computing Workshop and Challenges

June 1st, 2021 Daniela Lopez de Luise
]

*************** Call for Papers/Participation ***************
The Third Workshop and Challenge on Deep Learning for Geometric Computing in conjunction with ICCV 2021
Computer vision approaches have made tremendous efforts toward understanding shape from various data formats, especially since entering the deep learning era. Although accurate results have been obtained in detection, recognition, and segmentation, there is less attention and research on extracting topological and geometric information from shapes. These geometric representations provide compact and intuitive abstractions for modeling, synthesis, compression, matching, and analysis. Extracting such representations is significantly different from segmentation and recognition tasks, as they contain both local and global information about the shape. To attract attention of researchers from computer vision, computational geometry, computer graphics, and machine learning to this branch of problems, we organize the third edition of “Deep Learning for Geometric Computing” workshop at ICCV 2021. The workshop encapsulates competitions with prizes, proceedings, keynotes, paper presentations, and a fair and diverse environment for brainstorming about future research collaborations.
*************** Call for competition participation ***************
We are hosting seven competition tracks in two main domains: The SkelNetOn Challenge (2D) and The ABC Challenge (3D), implemented as independent contests available at Codalab.
*** The SkelNetOn Challenge ***
The SkelNetOn Challenge is structured around shape understanding in four domains. We provide shape datasets and some complementary resources (e.g, pre/post-processing, sampling, and data augmentation scripts) and the testing platform.
Submissions to the challenge will perform one of the following tasks:
– Shape pixels to skeleton pixels https://competitions.codalab.org/competitions/21169
– Shape points to skeleton points https://competitions.codalab.org/competitions/21172
– Shape pixels to parametric curves https://competitions.codalab.org/competitions/21175
– Natural image pixels to skeleton pixels https://competitions.codalab.org/competitions/24536 
*** The ABC Challenge ***
The ABC Challenge serves as a testbed for common shape analysis and geometry processing tasks. We supplement the challenge with additional software libraries, sets of large-scale standardized benchmarks (data splits, resolutions, and targets), and implementations of evaluation metrics. The first ABC challenge will be hosting a three-track contest on geometry processing, including:
– Estimation of non-oriented normals https://competitions.codalab.org/competitions/24253
– Geometric shape segmentation https://competitions.codalab.org/competitions/25087
*************** Call for paper submissions ***************
We will have an open submission format where i) participants in the competition will be required to submit a paper, or ii) researchers can share their novel unpublished research in deep learning for geometric computing. The top submissions in each category will be invited to present their work during the workshop and will be published in the workshop proceedings. The workshop will also honor the best paper and the best student paper.
Although we encourage all submissions to benchmark their results on the evaluation platform, there are other relevant research areas that our datasets do not address. For those areas, the scope of the submissions may include but is not limited to the following general topics:
    Boundary extraction from 2D/3D shapes
    Geometric deep learning on 3D and higher dimensions
    Generative methods for parametric representations
    Novel shape descriptors and embedding for geometric deep learning
    Deep learning on non-Euclidean geometries
    Transformation invariant shape abstractions
    Shape abstraction in different domains
    Synthetic data generation for data augmentation in geometric deep learning
    Comparison of shape representations for efficient deep learning
    Novel kernels and architectures specifically for 3D generative models
    Eigen-spectra analysis and graph-based approaches for 3D data
    Applications of geometric deep learning in different domains
    Learning-based estimation of shape differential quantities
    Detection of geometric feature lines from 3D data, including 3D point clouds and depth images
    Geometric shape segmentation, including patch decomposition and sharp lines detection
The CMT site for paper submissions is https://cmt3.research.microsoft.com/DLGC2021 . Each submitted paper must be 4-8 pages excluding references. Please refer to the ICCV author submission guidelines for instructions at http://iccv2021.thecvf.com/node/4#submission-guidelines. The review process will be double-blind but the papers will be linked to any associated challenge submissions. Selected papers will be published in IEEE ICCVW proceedings, visible in IEEE Xplore and on the CVF Website.
*************** Awards ***************
The winning submission in each seven track will receive a prize (either cash or equipment) provided by the workshop sponsors. The top submissions in each category with accepted papers in the workshop will be chosen as finalists and will be invited to present their research in the spotlight session.
*************** Important dates ***************
    Challenges Launch for Submissions: May, 07, 2021
    Second Phase for Submissions: July, 22, 2021
    Challenges Close for Submissions: August 1, 2021
    Abstract Submission Deadline: July 26, 2021
    Paper Submission Deadline: August 1, 2021
    Acceptance Notification: August 11, 2021
    Camera Ready Due: August 17, 2021
    Workshop (full day): October 11, 2021
***************Organizers***************
    Ilke Demir, Sr. Staff Research Scientist, Intel Corporation
    Alexey Artemov, Research Scientist, Skolkovo Institute of Science and Technology
    Dena Bazazian, Senior Research Associate, University of Bristol
    Bernhard Egger, Postdoctoral Researcher, MIT
    Géraldine Morin, Professor, University of Toulouse
    Kathryn Leonard, Professor of Computer Science, Occidental College
    Evgeny Burnaev, Associate Professor, Skolkovo Institute of Science and Technology
    Adarsh Krishnamurthy, Associate Professor, Iowa State University
    Daniele Panozzo, Assistant Professor, Courant Institute of Mathematical Sciences, New York University
    Albert Matveev, Ph.D. student, Skolkovo Institute of Science and Technology
    Denis Zorin, Professor of Computer Science and Mathematics, Chair of Computer Science Department Courant Institute of Mathematical Sciences, New York University
    Rana Hanocka, Ph.D. student, Tel Aviv University
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Call for papers Iccke2021

June 1st, 2021 Daniela Lopez de Luise

ICCKE 2021
October 28-29, 2021
Ferdowsi University of Mashhad, Iran
Call for Paper
The International Conference on Computer and Knowledge Engineering (ICCKE) is an online annual conference for the presentation of cutting-edge research contributions in the fields of computer and knowledge engineering. The conference will be held online. ICCKE 2020 is the 10th virtual forum to bring together leading scientists in the field. There are some limited registration grants for international authors, applied after paper acceptance. Accepted papers of all previous venues have been indexed by IEEE. ICCKE2021 accepted and presented papers will also be considered to be published in IEEE Xplore.
Paper Submission Deadline is June 21, 2021
The scope of the conference includes but is not limited to the following major topics:

  • Software and Knowledge Engineering
  • Machine Vision and its Applications
  • Machine Learning and its Applications
  • Computer Network and Security
  • Computer Architecture and Digital Design

Paper Submission
Authors are encouraged to submit full papers presenting new research. Submitted papers must not have been published elsewhere, nor be under review by another conference or journal. Full papers will be reviewed by expert referees active in the field to ensure relevance to the conference and high quality. All submissions should be written in English with a maximum of A4-size six (6) printed pages including figures and tables.

Paper Presentation

Accepted papers must be presented by author(s) personally to be published in the conference proceedings. The presentation will be online and should be in English.

Important Dates

Paper submission deadline: June 20, 2021
Notification of acceptance: September 11, 2021
Camera-ready deadline: September 25, 2021
Early registration deadline: September 25, 2021
Registration deadline: October 12, 2021
Conference Dates: October 28 – 29, 2021

The conference poster is attached to this email.
For other information about guide for authors and important dates, please refer to the conference web site and contact us section.

Contact us
Email: iccke2021@um.ac.ir
Website: iccke2021.um.ac.ir
Tel: +985138806059

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Workshop on Probabilistic Logic Programming (Deadline August 1st)

June 1st, 2021 Daniela Lopez de Luise

A workshop of 37th International Conference on Logic Programming
September 20-27, 2021,
University of Porto, Portugal
http://stoics.org.uk/plp/plp2021

*Note that the event will be virtual*

** Deadline for submissions: August 1st 2021

Overview

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