Attentive Models in Vision Call for Papers


Research Topic

"Attentive Models in Vision"

Computer Vision Section | Frontiers in Computer Science



Apologies for multiple posting
Please distribute this call to interested parties



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.



The topics of interest include but are not limited to:

    Saliency prediction and salient object detection

    Applications of human attention in Vision

    Visualization of attentive maps for Explainability of Deep Networks

    Use of Explainable-AI techniques to improve any aspect of the
    network (generalization, robustness, and fairness)

    Applications of attentive operators in the design of Deep Networks

    Transformer-based or attention-based models for Computer Vision
    tasks (e.g. classification, detection, segmentation)

    Transformer-based or attention-based models to combine Vision with
    other modalities (e.g. language, audio, speech)

    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

    Computational issues in attentive models

    Applications of attentive models (e.g., robotics and embodied AI,
    medical imaging, document analysis, cultural heritage)



    Abstract Submission Deadline: September 30th, 2021

    Paper Submission Deadline: January 31st, 2022

Research topic page:

Click here to participate:

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.

All submitted articles are peer reviewed.

All published articles are subject to article processing charges
(APCs). Frontiers works with leading institutions to ensure
researchers are supported when publishing open access. See if your
institution has a payment plan with Frontiers or apply to the
Frontiers Fee Support program.

If you wish to know more about Frontiers publishing and contribution
process, please head to the following sections:

    Collaborative peer review

    Author guidelines

    Open Access, publishing fees, and waivers



    Marcella Cornia, University of Modena and Reggio Emilia (Italy)

    Luowei Zhou, Microsoft (United States)

    Ramprasaath R. Selvaraju, Saleforce Research (United States)

    Prof. Xavier Giró-i-Nieto, Universitat Politecnica de Catalunya (Spain)

    Prof. Jason Corso, Stevens Institute of Technology  (United States)