Special issue on deep learning from aerial image

March 30th, 2021 Daniela Lopez de Luise

Call for papers in Remote Sensing. Special issue: Object Detection from Aerial and Space Platforms Using Deep Learning Methods. 
Topics:
  • Object detection methods.
  • Object change detection and monitoring methods.
  • High-quality datasets for object detection and identification.
  • Transfer learning methods.
  • Image segmentation methods.
  • Object detection and identification using multi-source and multi-modal data.
  • Similarity search methods.
  • Space object detection and recognition.
  • Embedded intelligent computer vision algorithms.

Remote Sensing is one of MDPI's open access journals (indexed in Web of Science, current Impact Factor 4.509). For more details please visit the website:

Dr. Antonio Pertusa, 
Dr. Pablo Gil
Dr. Antonio-Javier Gallego
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Curso a Distancia: Riesgo eléctrico y Seguridad aplicada a la Operación y el mantenimiento de instalaciones de baja, media y alta tensión.

March 30th, 2021 Daniela Lopez de Luise

Riesgo eléctrico y Seguridad aplicada a la Operación y el mantenimiento de instalaciones de baja, media y alta tensión.
Curso de Capacitación a Distancia vía Web,
Modalidad On line en Vivo
17 al 28 de Mayo de 2021.
Inscripción Abierta.
OBJETIVOS
El objetivo general será desarrollar habilidades entre los alumnos a través del estudio y la identificación de los distintos aspectos de seguridad a tener en cuenta en la operación y el mantenimiento de las instalaciones y equipamiento de baja, media y alta tensión.

Partiendo de una visión general sobre las características particulares que reviste el tema, se irá paso a paso transitando por el análisis técnico teórico al metodológico práctico.

El objetivo general es ofrecer a los alumnos herramientas que les permitan:

• Conceptuar el tema de riesgo eléctrico y sus consecuencias.
• Identificar claramente el riesgo eléctrico de la actividad de operación.
• Identificar claramente el riesgo eléctrico de la actividad de mantenimiento.
• Identificar las normas internacionales relacionadas.
• Afirmar conceptos fundamentales del arco eléctrico y sus consecuencias.
• Afirmar conceptos básicos sobre los efectos de la corriente sobre el cuerpo humano.
• Valorizar los aspectos de seguridad en trabajos sin tensión.
• Valorizar los aspectos de seguridad en trabajos con tensión (TCT).
• Valorizar los aspectos de seguridad en trabajos no eléctricos que se realicen en la proximidad de instalaciones bajo tensión.

DESTINATARIOS
Ingenieros, técnicos e idóneos involucrados en los procesos de coordinación, supervisión, ejecución y soporte de distintas áreas de generación, transmisión y distribución de energía eléctrica.
El desarrollo didáctico del curso ha sido diseñado para profesionales y especialistas que trabajen en la temática indicada.
 
Temario
Docente
Información General
Formulario de Inscripción
ORGANIZA

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Special issue on robotic mobile manipulation

March 30th, 2021 Daniela Lopez de Luise

Call for papers in Electronics. Special issue: Advances in Robotic Mobile Manipulation
Topics:
-Location of objectives for grasping.
-Road and trajectory planning to reach the handling areas.
-Sensing and planning in object manipulation.
-Piloting, orientation, and stabilization of the autonomous vehicle during handling.
-Grasping stability assessment using tactile perception, visual perception, or both.
-Control strategies for object manipulation.
-Manipulation of object in-hand with multi-fingered hands.
–Planning of tasks for grasping, transport, and placement of objects.
-Learning of grasping, manipulation, and navigation skills.

Electronics is one of MDPI's open access journals (indexed in Web of Science, current Impact Factor 2.412). For more details please visit the website:

Dr. Pablo Gil
Dr. Francisco A. Candelas
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2nd CFP – CVPR 2021 “Learning from Limited and Imperfect Data” (L2ID) Workshop & Challenges

March 30th, 2021 Daniela Lopez de Luise

Learning from Limited and Imperfect Data (L2ID) Workshop & Challenges

In conjunction with the Computer Vision and Pattern Recognition Conference (CVPR) 2021
June 19-25 2021, Virtual Online

https://l2id.github.io/

******************************
CALL FOR PAPERS & CHALLENGE PARTICIPATION

Learning from limited or imperfect data (L^2ID) refers to a variety of studies that attempt to address challenging pattern recognition tasks by learning from limited, weak, or noisy supervision. Supervised learning methods, including Deep Convolutional Neural Networks, have significantly improved the performance of many problems in the field of computer vision. However, these approaches are notoriously “data hungry”, which makes them sometimes not practical in many real-world industrial applications. The issue of availability of large quantities of labeled data becomes even more severe when considering visual classes that require annotation based on expert knowledge (e.g., medical imaging), classes that rarely occur, or object detection and instance segmentation tasks where the labeling requires more effort. To address this problem, many efforts have been made to improve robustness to this scenario. The goal of this workshop is to bring together researchers to discuss emerging new technologies related to visual learning with limited or imperfectly labeled data.

We will have two groups of challenges this year, including for localization and few-shot classification. Check the website for all the L2ID challenges:

Localization:
Track 1 – Weakly Supervised Semantic Segmentation
Track 2 – Weakly supervised product detection and retrieval
Track 3 – Weakly-supervised Object Localization
Track 4 – High-resolution Human Parsing

Few Shot Classification:
Track 1 – Cross Domain, small scale
Track 2 – Cross Domain, large scale
Track 3 – Cross Domain, larger number of classes

******************************
TOPICS

• Few-shot learning for image classification, object detection, etc.
• Cross-domain few-shot learning
• Weakly-/semi-supervised learning algorithms
• Zero-shot learning, Learning in the “long-tail” scenario
• Self-supervised learning and unsupervised representation learning
• Learning with noisy data
• Any-shot learning – transitioning between few-shot, mid-shot, and many-shot training
• Optimal data and source selection for effective meta-training with a known or unknown set of target categories
• Data augmentation
• New datasets and metrics to evaluate the benefit of such methods
• Real world applications such as object semantic segmentation/detection/localization, scene parsing, video processing (e.g. action recognition, event detection, and object tracking)

This is not a closed list, we welcome other interesting and relevant research for L^2ID.

******************************
IMPORTANT DATES

Paper submission deadline: March 25th, 2021
Notification to authors: April 8th, 2021
Camera-ready deadline: April 20th, 2021

The contributions can have two formats
– Extended Abstracts of max 4 pages (excluding references)
– Papers of the same lenght of CVPR submissions

We encourage authors who wants to present and discuss their ongoing work to choose the Extended Abstract format.
According to the CVPR rules, extended abstracts will not count as archival.

The submissions should be uploaded through CMT: https://cmt3.research.microsoft.com/LLID2021

******************************
WORKSHOP ORGANIZERS:
Zsolt Kira (Georgia Tech, USA)
Shuai (Kyle) Zheng (Dawnlight Technologies Inc, USA)
Noel C. F. Codella (Microsoft, USA)
Yunchao Wei (University of Technology Sydney, AU)
Tatiana Tommasi (Politecnico di Torino, IT)
Ming-Ming Cheng (Nankai University, CN)
Judy Hoffman (Georgia Tech, USA)
Antonio Torralba (MIT, USA)
Xiaojuan Qi (University of Hong Kong, HK)
Sadeep Jayasumana (Google, USA)
Hang Zhao (MIT, USA)
Liwei Wang (Chinese University of Hong Kong, HK)
Yunhui Guo (UC Berkeley/ICSI, USA)
Lin-Zhuo Chen (Nankai University, CN)

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ICRA2021 CFP: Workshop on Perception and Action in Dynamic Environments – Submission deadline May 10

March 30th, 2021 Daniela Lopez de Luise

General-purpose autonomy requires robots to interact with a constantly
dynamic and uncertain world. Our workshop brings together amazing
keynote speakers on this topic. We encourage the submission of full
research papers or extended abstract, please submit even if your work is
only preliminary! In conjunction with the workshop, we will held the
DodgeDrone Challenge, where participants can build navigation algorithms
for drones flying through a forest! The winner of the competition will
be awarded a Skydio2 drone directly awarded from Skydio Autonomy!

Website: https://uzh-rpg.github.io/PADE-ICRA2021/

==============
Important dates
==============
   *   Paper Submission deadline: May 10, 2021 Any time on Earth
   *   Notification date: May 24, 2021
   *   Challenge Submission deadline: June 1, 2021 Any time on Earth
   *   Workshop date: June 4, 2021 from 3pm to 8pm GMT (London time),
online.

=======================
Confirmed invited speakers
=======================
   *   Chelsea Finn and Annie Xie, Stanford
   *   Raquel Urtasun, University of Toronto
   *   Richard Newcombe, Facebook Reality Labs
   *   Hayk Martiros, Skydio Autonomy
   *   Katherine J. Kuchenbecker, Max Planck Institute for Intelligent
Systems
   *   Alexsandra Faust, Google Brain
   *   Wolfram Burgard, University of Freiburg and Toyota Research Institute

=================
Overview and topics
=================

Humans and animals have an innate capacity to make predictions about
their surroundings, which allows them to react to both static and
dynamic obstacles during an action. Thanks to this ability, for example,
a seagull can catch a fast-moving fish in a short amount of time. In
contrast, artificial agents struggle to interact with complex and
dynamic environments and often rely either on the assumption that the
world is static or on simplified motion models of their surroundings.
This workshop will bring together researchers coming from different
backgrounds (computer vision, machine learning, and robotics) and
applications, to discuss existing solutions, research problems, and the
way forward to make robots interact with a permanently moving world.
Besides the usual mix of invited talks and poster presentations, we will
organize the DodgeDrone challenge, where participants will need to
develop perception and control algorithms to navigate a drone in a
highly dynamic environment.
   *   Perception: state estimation, object detection, free space
detection, etc.
   *   Simulation and modeling
   *   Transfer from simulation to reality
   *   Machine Learning for Robotics: end-to-end learning, learning from
demonstration, reinforcement learning
   *   Control, from high-level planning to high-fidelity tracking.
   *   Manipulation in unstructured environments.
   *   Application-specific challenges: interaction with humans,
navigation in the wild, AR/VR in dynamic scenes, etc.

==========
Submission
==========
All submitted papers will be reviewed by at least two international
experts on the basis of technical quality, relevance, significance, and
clarity. We accept extended abstracts (2-4 pages), experiences’ reports
(2-4 pages), or full research papers (up to 6 pages). We also encourage
submission of live demos and working systems (up to 2 pages). All
accepted papers will appear on the workshop website. The paper version
should be a paper in pdf standard IEEE format. Accepted paper will be
made available on the website, and authors will be invited to give a
presentation about their work. Submission website:
https://easychair.org/conferences/?conf=pade2021.

==========
Challenge
==========
The DodgeDrone challenge revisits the popular dodgeball game in the
context of autonomous drones. Specifically, participants will have to
code navigation policies to fly drones between waypoints while avoiding
dynamic obstacles. Drones are fast but fragile systems: as soon as
something hits them, they will crash! Since objects will move towards
the drone with different speeds and accelerations, smart algorithms are
required to avoid them!

The competition consists of two challenges: (i) navigation in a static
environment, and (ii) navigation in a dynamic environment. The
navigation policy can only rely on on-board perception (dense depth,
agent and goal location). These two modalities will help participants to
concentrate on different aspects of the navigation algorithm. Two
environments are used for the competition: a simple and surrealistic one
(which you should reserve for training and development); and a testing
one consisting of a photorealistic forest, where drones have to avoid
the vegetation, as well as the rocks and birds that will obstruct their
path.

A demo video can be find at the following link: https://youtu.be/ZC1jfh2074o

=========
Organizers
=========
   *   Antonio Loquercio, University and ETH Zurich, Switzerland
   *   Davide Scaramuzza, University and ETH Zurich, Switzerland
   *   Luca Carlone, Massachusetts Institute of Technology (MIT), USA
   *   Markus Ryll, Technical University of Munich, Germany

On behalf of the organizers,
Davide Scaramuzza

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