[visionlist] [CfP] Extensions: ECCV 2020 Workshop - Beyond mAP: Reassessing the Evaluation of Object Detectors + 3rd Probabilistic Object Detection (PrOD) Challenge

[David Hall]

Call for Papers Extension:
ECCV 2020 Workshop - Beyond mAP: Reassessing the Evaluation of Object Detectors
3rd Probabilistic Object Detection (PrOD) Challenge



Workshop link

https://nikosuenderhauf.github.io/roboticvisionchallenges/eccv2020


Challenge link

https://competitions.codalab.org/competitions/20597



We are pleased to announce an extension to the deadlines of both our ECCV 2020 workshop and associated Probabilistic Object Detection (PrOD) Challenge. Updated dates and workshop/challenge details below.


New Dates

  *   Final Challenge Submission - 05 August 2020 Midnight UTC
  *   Paper  Submission (workshop and challenge) - 07 August 2020 Midnight UTC
  *   Paper + Competition Results  - 12 August 2020
  *   Videos for Workshop Due - 20 August 2020
  *   Workshop Interactive Sessions - 28 August 2020 UTC+1 (exact times TBC)


Workshop summary

This workshop assesses current evaluation procedures for object detection, highlights their shortcomings with respect to practical applications and opens discussion for possible improvements.

Through a focus on evaluation using challenges, the object detection community has been able to quickly identify which methods are effective by examining performance metrics. However, as this technological boom progresses, it is important to assess whether our evaluation metrics and procedures adequately align with how object detection will be used in practical applications such as robotics. Quantitative results should be easily reconciled with a detector’s performance in applied tasks. This workshop provides a forum to discuss these ideas and evaluate whether current standards meet the needs of the object detection community.



Call for Papers

We invite authors to contribute papers to the workshop. Topics of interest comprise, but are not limited to:

  *   New evaluation measures/metrics for object detection
  *   New evaluation/visualization tools to analyze object detection systems
  *   New evaluation procedures for better understanding object detection performance
  *   Examinations of current evaluation procedures
  *   New datasets designed to examine specific challenges in object detection
  *   New detection methods that provide contributions/insights unrewarded by current evaluation procedures (e.g. improved detector calibration, probabilistic object detection, etc.)

Author Instructions

  *   Submissions must follow the ECCV format and be up to 4 pages in length including references
  *   It is accepted if this is an abbreviated version of a larger paper published elsewhere if properly referenced
  *   Submit your paper through CMT (link<https://cmt3.research.microsoft.com/BMREOD2020>)
  *   Accepted papers will provide a short video outlining their work which will be part of the ECCV online workshop system (details to come in the future so stay tuned to workshop website).

PrOD Competition

You may also be accepted to present your work at our conference if you perform well on the 3rd Probabilistic Object Detection (PrOD) Challenge.



This is a computer vision challenge designed to be more applicable for robotics applications where competitors must operate using video data, unusual viewing angles, and where you must estimate your spatial and semantic uncertainty.



Details can be found at the workshop website as well as the main competition page below.



(https://competitions.codalab.org/competitions/20597)



If you have any questions about the workshop or the challenge don't hesitate to get in contact.



Best regards,



Dr David Hall



Research Fellow

Robotic Vision Benchmarking and Evaluation Project

Australian Centre for Robotic Vision

Queensland University of Technology



e-mail: d20.hall at qut.edu.au

Phone: +61 7 31380656

ORCiD: 0000-0002-5520-0128<https://orcid.org/0000-0002-5520-0128>

Website: https://sites.google.com/view/davidhallcv/home


<https://sites.google.com/view/davidhallcv/home>
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