[visionlist] [CFP] Research Topic "Attentive Models in Vision" - Computer Vision Section of Frontiers in Computer Science

[Marcella Cornia]

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Research Topic

“Attentive Models in Vision”

Computer Vision Section | Frontiers in Computer Science
https://www.frontiersin.org/research-topics/23980/attentive-models-in-vision

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=== SUBMISSIONS ARE OPEN!!! ====



Apologies for multiple posting

Please distribute this call to interested parties



AIMS AND SCOPE

===============

The modeling and replication of visual attention mechanisms have been
extensively studied for more than 80 years by neuroscientists and more
recently by computer vision researchers, contributing to the formation of
various subproblems in the field. Among them, saliency estimation and
human-eye fixation prediction have demonstrated their importance in
improving many vision-based inference mechanisms: image segmentation and
annotation, image and video captioning, and autonomous driving are some
examples. Nowadays, with the surge of attentive and Transformer-based
models, the modeling of attention has grown significantly and is a pillar
of cutting-edge research in computer vision, multimedia, and natural
language processing. In this context, current research efforts are also
focused on new architectures which are candidates to replace the
convolutional operator, as testified by recent works that perform image
classification using attention-based architectures or that combine vision
with other modalities, such as language, audio, and speech, by leveraging
on fully-attentive solutions.

Given the fundamental role of attention in the field of computer vision,
the goal of this Research Topic is to contribute to the growth and
development of attention-based solutions focusing on both traditional
approaches and fully-attentive models. Moreover, the study of human
attention has inspired models that leverage human gaze data to supervise
machine attention. This Research Topic aims to present innovative research
that relates to the study of human attention and to the usage of attention
mechanisms in the development of deep learning architectures and enhancing
model explainability.

Research papers employing traditional attentive operations or employing
novel Transformer-based architectures are encouraged, as well as works that
apply attentive models to integrate vision and other modalities (e.g.,
language, audio, speech, etc.). We also welcome submissions on novel
algorithms, datasets, literature reviews, and other innovations related to
the scope of this Research Topic.


TOPICS

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The topics of interest include but are not limited to:


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   Saliency prediction and salient object detection
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   Applications of human attention in Vision
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   Visualization of attentive maps for Explainability of Deep Networks
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   Use of Explainable-AI techniques to improve any aspect of the network
   (generalization, robustness, and fairness)
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   Applications of attentive operators in the design of Deep Networks
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   Transformer-based or attention-based models for Computer Vision tasks
   (e.g. classification, detection, segmentation)
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   Transformer-based or attention-based models to combine Vision with other
   modalities (e.g. language, audio, speech)
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   Transformer-based or attention-based models for Vision-and-Language
   tasks (e.g., image and video captioning, visual question answering,
   cross-modal retrieval, textual grounding / referring expression
   localization, vision-and-language navigation)
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   Computational issues in attentive models
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   Applications of attentive models (e.g., robotics and embodied AI,
   medical imaging, document analysis, cultural heritage)



IMPORTANT DATES

=================

   -

   Paper Submission Deadline: January 31st, 2022


Research topic page:
https://www.frontiersin.org/research-topics/23980/attentive-models-in-vision

Click here to participate:
https://www.frontiersin.org/research-topics/23980/attentive-models-in-vision/participate-in-open-access-research-topic

By expressing your interest in contributing to this collection, you will be
registered as a contributing author and will receive regular updates
regarding this Research Topic.


SUBMISSION GUIDELINES
======================
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TOPIC EDITORS

==============

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   Marcella Cornia, University of Modena and Reggio Emilia (Italy)
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   Luowei Zhou, Microsoft (United States)
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   Ramprasaath R. Selvaraju, Saleforce Research (United States)
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   Prof. Xavier Giró-i-Nieto, Universitat Politecnica de Catalunya (Spain)
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   Prof. Jason Corso, Stevens Institute of Technology  (United States)



-- 
*Marcella Cornia*, PhD
AImageLab, Dipartimento di Ingegneria "Enzo Ferrari"
Università degli Studi di Modena e Reggio Emilia
e-mail: marcella.cornia at unimore.it
phone: +39 059 2058790
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