[visionlist] Call for participation -- “Performances Measures in Visual Detection and Their Optimization”, CVPR2022 Tutorial

Sinan KALKAN sinankalkan at gmail.com
Wed Jun 22 02:58:31 -04 2022

We cordially invite those interested to our CVPR2022 virtual tutorial
on Performance
Measures in Visual Detection and Their Optimization to be held online on 30
June 2022.

Tutorial Website:

About the Tutorial

Many vision applications require identifying objects and object-related
information in images. Such identification can be performed at different
levels of detail, which are addressed by different visual detection tasks
such as “object detection” for identifying labels of objects and boxes
bounding them, “keypoint detection” for finding keypoints on objects,
“instance segmentation” for identifying the classes of objects and
localizing them with masks, and “panoptic segmentation” for both semantic
segmentation of background classes and instance segmentation of objects.
Accurately evaluating performances of these methods is crucial for
developing better solutions.

Accordingly, in this tutorial, we aim to extensively delve into the
evaluation of visual detectors. Within the scope of our tutorial, we will
first cover the basics of evaluating visual detectors in order to allow
someone not familiar with visual detection to grasp the basics. Then, we
will introduce the Localisation Recall Precision (LRP) Error [1,2] and
present thorough comparative both theoretical and comparative analyses with
Average Precision (AP) and Panoptic Quality (PQ) [3] on various visual
detection tasks. Finally, we will discuss bridging the gap between training
and evaluation by directly optimizing AP and LRP, which involves a
non-differentiable ranking step that is difficult to optimize using
conventional gradient-based methods.

Program (in CST)

Date: 30 June

11.00am-11:50noon -- Part I: The Basics of Evaluating Visual Detectors [50

- Motivation and Introduction on Visual Detection Tasks

- Performance Measures in Visual Detection: Average Precision, Panoptic
Quality, Localization Recall Precision (LRP)

- Recent Advances: Probabilistic Detection Quality, AP-fixed, AP-pool,
Boundary IoU, Optimal Correction Cost

11:50am-12:00noon -- Break

12:00noon -- 12:50pm -- Part II: An Analysis of Performance Measures and
Localisation-Recall-Precision Error [50 min]:

- An Analysis of Performance Measures: Important features for a performance
measure, evaluating AP and PQ in terms of important features

- Localisation-Recall-Precision Error: Definition, Analysis, Optimal LRP
Error, s-LRP Curves, Theoretical and Empirical Comparison of LRP Error with
AP and PQ

12:50pm-01:00pm -- Break

01:00pm-01:50pm -- Part III: Optimization of Performance Measures [50 min]:

- Identity Update to Optimize Ranking-based Loss Functions

- Average Precision Loss for Classification

- Average Localisation-Recall-Precision Loss for Object Detection

- Rank & Sort Loss for Object Detection and Instance Segmentation

01:50pm-02:00pm -- Q&A

Please check our webpage for up-to-date program:

Participation Details

Participation in our tutorial will be via the CVPR2022 platform and
therefore will require registration to the conference.

Organizing Committee

Emre Akbas, Sinan Kalkan, Kemal Oksuz


[1] K. Oksuz, B. C. Cam, S. Kalkan*, E. Akbas*, "One Metric to Measure them
All: Localisation Recall Precision (LRP) for Evaluating Visual Detection
Tasks", IEEE Transactions on Pattern Analysis and Machine Intelligence
(PAMI), in press, 2022. [Paper] [Code]

[2] K. Oksuz, B. C. Cam, E. Akbas, S. Kalkan, "Localization Recall
Precision (LRP): A New Performance Metric for Object Detection", European
Conference on Computer Vision (ECCV), pp. 521-537, Springer, 2018. [Paper]

[3] Kirillov, A., He, K., Girshick, R., Rother, C., & Dollár, P. "Panoptic
segmentation". IEEE/CVF Conference on Computer Vision and Pattern
Recognition (pp. 9404-9413), 2019.

Sinan KALKAN, Assoc. Prof. Dr.

Dept. of Computer Engineering
Middle East Technical University
Ankara, TURKEY
Web: https://ceng.metu.edu.tr/~skalkan/
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