PRL
Computer Vision & Pattern Recognition
Pattern Recognition Letters (Impact factor: 2.81) Call for Papers
Special Issue on
Deep Learning for Precise and Efficient Object Detection
Submission period: Dec. 1-31, 2020, First notification: Mar. 1,2021
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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 to:
Important Dates
Submission period: Dec. 1-31, 2020
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 select as article type: VSI: DL4PEOD when submit manuscripts.
Guest Editors
Dr. Yanwei Pang, Tianjin University, China, pyw@tju.edu.cn
Dr. Jungong Han, Warwick University, U.K., jungong.han@warwick.ac.uk
Dr. Xin Lu, Adobe Inc., U.S.A., xinl@adobe.com
Dr. Nicola Conci, University of Trento, Italy, nicola.conci@unitn.it