[visionlist] [CfP] Pattern Rec. Letters, Elsevier: SI Deep Learning for Precise and Efficient Object Detection

jungong han jungonghan77 at gmail.com
Sun Jan 3 12:01:01 -04 2021

Dear researcher,
  The submission deadline has been extended to 15. Jan, 2021

Aim and Scopes

Object detection is one of the most challenging and important tasks of
computer vision and is widely used in applications such as autonomous
vehicle, biometrics, video surveillance, and human-machine interactions. In
the past five years, significant success has been achieved with the
development of deep learning, especially deep convolutional neural
networks. Typical categories of advanced object detection methods are
one-stage, two-stage, and anchor-free methods. Nevertheless, the
performance in accuracy and efficiency is far from satisfying. On the one
hand, the average precision of state-of-the-art object detection methods is
very low (e.g., merely about 40% on the COCO dataset). The performance is
even worse for small and occluded objects. On the another hand, to obtain
precision the detection speed is very low. It is challenging to get a
satisfying trade-off between the detection precision and speed. Therefore,
much efforts have to be engaged to remarkably improve the performance of
object detection in both precision and efficiency.

This special issue will publish papers presenting state-of-the-art methods
in dealing with the challenging problems of object detection within the
framework of deep learning. We invite authors to submit manuscripts that
are highly related to the topics of this special issue and which have not
been published before. The topics of interest include, but are not limited

   -  Anchor and Anchor-free object detection
   -  Detecting small or occluded objects
   -  Context and attention mechanism for object detection
   -  Fast object detection algorithms
   -  New backbone for object detection
   -  Architecture search for object detection
   -  3D object detection
   -  Object detection in challenging conditions
   -  Handling scale problems in object detection
   -  Improving localization accuracy
   -  Fusion of point cloud and images for object detection
   -  Relationship between object detection and other computer vision tasks.
   -  Large-scale datasets for object detection

Important Dates

Submission period: Jan. 15, 2021

First notification to authors: Mar. 1, 2021

Submission of revised papers: Apr. 15, 2021

Final notification to authors: June 15, 2021

Online publication: Jul. 1, 2021

Submission of Manuscripts

Prospective authors should write manuscripts according to the Guide for
Authors of Pattern Recognition Letters available at the website
https://ees.elsevier.com/prletters/. Please use article type name by:

Guest Editors

Dr. Yanwei Pang, Tianjin University, China, pyw at tju.edu.cn
<http://nonsolus/st@r/admin/tasks/pyw@tju.edu.cn>, MGE

Dr. Jungong Han, Warwick University, U.K., jungong.han at warwick.ac.uk

Dr. Xin Lu, Adobe Inc., U.S.A., xinl at adobe.com

Dr. Nicola Conci, University of Trento, Italy, nicola.conci at unitn.it
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