Human Visual Saliency and Artificial Neural Attention in Deep Learning

  in Special Issue   Posted on June 15, 2019

Information for the Special Issue

Submission Deadline: Wed 10 Jun 2020
Journal Impact Factor : 3.317
Journal Name : Neurocomputing
Journal Publisher:
Website for the Special Issue: https://www.journals.elsevier.com/neurocomputing/call-for-papers/human-visual-saliency
Journal & Submission Website: https://www.journals.elsevier.com/neurocomputing

Special Issue Call for Papers:

1) Aim and Scope

Human visual system can process large amounts of visual information (108-109 bits per second) in parallel. Such astonishing ability is based on the visual attention mechanism which allows human beings to selectively attend to the most informative and characteristic parts of a visual stimulus rather than the whole scene. Modeling visual saliency is a long-term core topic in cognitive psychology and computer vision community. Further, understanding human gaze behavior during social scenes is essential for understanding Human-Human Interactions (HHIs) and enabling effective and natural Human-Robot Interactions (HRIs). In addition, the selective mechanism of human visual system inspires the development of differentiable neural attention in neural networks. Neural networks with attention mechanism are able to automatically learn to selectively focus on sections of input, which have shown wide success in many neural language processing and mainstream computer vision tasks, such as neural machine translation, sentence summarization, image caption generation, visual question answering, and action recognition. The visual attention mechanism also boosts biologically-inspired object recognition, including salient object detection, object segmentation, and object classification.

The list of possible topics includes, but is not limited to:

  • Visual attention prediction during static/dynamic scenes
  • Computational models for saliency/co-saliency detection in images/videos
  • Computational models for social gaze, co-attention and gaze-following behavior
  • Gaze-assistant Human-Robotics Interaction (HRI) algorithms and gaze-based Human-Human Interaction (HHI) models
  • Neural attention based NPL applications (e.g., neural machine translation, sentence summarization, etc)
  • Approaches for attention-guided object recognition, such as object classification, object segmentation and object detection.
  • Visual saliency for various applications (e.g., object tracking, human-machine interaction, and automatic photo editing, etc.)
  • Artificial attention and multi-modal attention based applications (e.g., network knowledge distillation, network visualization, image captioning, and visual question answering, etc.)
  • New benchmark datasets and evaluation metrics related to the aforementioned topics

2) Submission Guidelines

Authors should prepare their manuscripts according to the \”Instructions for Authors\” guidelines of \”Neurocomputing\” outlined at the journal website https://www.elsevier.com/journals/neurocomputing/0925-2312/guide-for-authors. To ensure that all manuscripts are correctly identified for inclusion into the special issue, it is important that authors select HVSAN when they reach the \”Article Type\” step in the submission process. All the papers will be peer-reviewed following the NEUROCOMPUTING reviewing procedures.

3) Important Dates

Submission: November 10, 2019 – January 10, 2020

Revised papers due: June 10, 2020

Final notification: August 10, 2020

4) Guest Editors

Dr Wenguan Wang (Managing Guest Editor)

Senior Scientist

Inception Institute of Artificial Intelligence, UAE

email: wenguanwang.ai@gmail.com
web page: https://sites.google.com/view/wenguanwang/

Dr Ming-Ming Cheng

Professor

Nankai University, China

email: cmm@nankai.edu.cn

web page: https://mmcheng.net/cmm/

Dr Haibin Ling

Professor

Temple University, USA

email: haibin.ling@gmail.com

web page: http://www.dabi.temple.edu/~hbling/

Dr Fatih Porikli

Professor

Australian National University, AUT

email: fatih.porikli@anu.edu.au

web page: http://www.porikli.com/

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