[visionlist] [CFP] Extedend Deadline "Attentive Models in Vision" (February 28, 2022) - Frontiers in Computer Vision

Marcella Cornia marcella.cornia at unimore.it
Tue Feb 8 05:27:42 -04 2022


Research Topic

“Attentive Models in Vision”

Computer Vision Section | Frontiers in Computer Science


Paper Submission Deadline: February 28, 2022

Apologies for multiple posting

Please distribute this call to interested parties




   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)



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 navigation)

   Computational issues in attentive models

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




   Paper Submission Extended Deadline: February 28, 2022

Research topic page:

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*Marcella Cornia*, PhD
Tenure-Track Assistant Professor (RTD-B)
Dipartimento di Educazione e Scienze Umane
Università degli Studi di Modena e Reggio Emilia
e-mail: marcella.cornia at unimore.it
phone: +39 059 2058790
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