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Attention please!

Erik Duval
2011 Proceedings of the 1st International Conference on Learning Analytics and Knowledge - LAK '11  
This paper will present the general goal of and inspiration for our work on learning analytics, that relies on attention metadata for visualization and recommendation.  ...  Moreover, recommendation can help to deal with the "paradox of choice" and turn abundance from a problem into an asset for learning.  ...  Much more importantly, the support, comments and feedback from my team and students have thought me much more than I will ever be able to teach them.  ... 
doi:10.1145/2090116.2090118 dblp:conf/lak/Duval11 fatcat:w35mvz5kojab3cjcwuqkik3s5y

Rapid learning in attention shifts: A review

Árni Kristjánsson
2006 Visual Cognition  
PRIMING IN A CONJUNCTIVE VISUAL SEARCH TASK In Kristja Ânsson, Wang, and Nakayama (2002) we have further investigated priming in visual search using a more challenging visual search task than the one  ...  To tie these findings to the present topic of how previous task history, in the short run, influences deployments of visual attention, we argued that the efficient search we observed, where explicit guidance  ... 
doi:10.1080/13506280544000039 fatcat:xsjslbbvirdx5ptrompsnmca3m

Deep Multimodal Neural Architecture Search [article]

Zhou Yu, Yuhao Cui, Jun Yu, Meng Wang, Dacheng Tao, Qi Tian
2020 arXiv   pre-print
By using a gradient-based NAS algorithm, the optimal architectures for different tasks are learned efficiently.  ...  In this paper, we devise a generalized deep multimodal neural architecture search (MMnas) framework for various multimodal learning tasks.  ...  Early NAS methods use the reinforcement learning to search neural architectures, which are computationally exhaustive [64, 65] .  ... 
arXiv:2004.12070v2 fatcat:424rhm5cknhpbklcn2obcgzvh4

Adaptive Feature Guidance: Modelling Visual Search with Graphical Layouts

Jussi P.P. Jokinen, Zhenxin Wang, Sayan Sarcar, Antti Oulasvirta, Xiangshi Ren
2019 International Journal of Human-Computer Studies  
The model suggests, for example, that (1) layouts that are visually homogeneous are harder to learn and more vulnerable to changes, (2) elements that are visually salient are easier to search and more  ...  A B S T R A C T We present a computational model of visual search on graphical layouts. It assumes that the visual system is maximising expected utility when choosing where to fixate next.  ...  Therefore, the main problem in visually searching graphical UIs becomes the problem of attention deployment: where to look next?  ... 
doi:10.1016/j.ijhcs.2019.102376 fatcat:bjk47k7iorfqnokwfj7v2lyf6a

Pose-Guided Multi-Granularity Attention Network for Text-Based Person Search [article]

Ya Jing, Chenyang Si, Junbo Wang, Wei Wang, Liang Wang, Tieniu Tan
2019 arXiv   pre-print
To further capture the phrase-related visual body part, a fine-grained alignment network (FA) is proposed, which employs pose information to learn latent semantic alignment between visual body part and  ...  To exploit the multilevel corresponding visual contents, we propose a pose-guided multi-granularity attention network (PMA).  ...  Recently, attention is widely used in person search, which selects either visual contents or textual information.  ... 
arXiv:1809.08440v3 fatcat:rb33zfv645at3nfh7qu7vcjqvi

Pose-Guided Multi-Granularity Attention Network for Text-Based Person Search

Ya Jing, Chenyang Si, Junbo Wang, Wei Wang, Liang Wang, Tieniu Tan
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
To further capture the phrase-related visual body part, a fine-grained alignment network (FA) is proposed, which employs pose information to learn latent semantic alignment between visual body part and  ...  To exploit the multilevel corresponding visual contents, we propose a pose-guided multi-granularity attention network (PMA).  ...  Recently, attention is widely used in person search, which selects either visual contents or textual information.  ... 
doi:10.1609/aaai.v34i07.6777 fatcat:e7o4adxewrb5ned33qnmjdisuq

Visual search and location probability learning from variable perspectives

Y. V. Jiang, K. M. Swallow, C. G. Capistrano
2013 Journal of Vision  
Do moving observers code attended locations relative to the external world or relative to themselves? To address this question we asked participants to conduct visual search on a tabletop.  ...  Goal-driven attention can be deployed to prioritize an environment-rich region.  ...  All authors contributed to the design. YVJ and CGC set up the experiments and collected the data. YVJ and KMS interpreted the data and wrote the paper.  ... 
doi:10.1167/13.6.13 pmid:23716120 fatcat:2p4tzf6k7rglzi47yi4ifulyrm

A Model for Calculating Saliency from Both Input Image and Memory

Toshio Endoh, Makoto Goto, Takashi Toriu
2002 IAPR International Workshop on Machine Vision Applications  
As a first step to implement human hnctions related to visual attention in computer vision, we developed a computational model for calculating the saliency map of an input image.  ...  an asymmetrical effect in visual search.  ...  Although this asymmetrical effect in visual search implies that the degree to which attention can be easily directed to a certain area of the image is influenced by visual experience, the model in the  ... 
dblp:conf/mva/EndohGT02 fatcat:gvqqxcpxvbcufdor3yv5tgcefy

Visionary: Vision architecture discovery for robot learning [article]

Iretiayo Akinola, Anelia Angelova, Yao Lu, Yevgen Chebotar, Dmitry Kalashnikov, Jacob Varley, Julian Ibarz, Michael S. Ryoo
2021 arXiv   pre-print
This is the first approach to demonstrate a successful neural architecture search and attention connectivity search for a real-robot task.  ...  We propose a vision-based architecture search algorithm for robot manipulation learning, which discovers interactions between low dimension action inputs and high dimensional visual inputs.  ...  This is done within Reinforcement Learning (RL)-based robot learning context, where in addition to learning the main architecture to generate visual features and combine them with action inputs, the robot  ... 
arXiv:2103.14633v1 fatcat:7j5656qfojfrphdhleqwt5gvea

A User Study on User Attention for an Interactive Content-based Image Search System

Mahmoud Artemi, Haiming Liu
2021 Conference on Human Information Interaction and Retrieval  
For contentbased image search systems, it is important to understand what users pay attention to, and thus engage users more in the search process.  ...  User attention is one of the fundamental indications of users' interests in search.  ...  The SS system used the active learning mechanism where data is abundant [28] . It enabled the users to provide feedback as an intent or preferences.  ... 
dblp:conf/chiir/Artemi021 fatcat:tcsago7gafcmrjokgtrimizmeq

Computational Models of Human Visual Attention and Their Implementations: A Survey

Akisato KIMURA, Ryo YONETANI, Takatsugu HIRAYAMA
2013 IEICE transactions on information and systems  
In particular, our objective is to carefully distinguish several types of studies related to human visual attention and saliency as a measure of attentiveness, and to provide a taxonomy from several viewpoints  ...  Exploiting this pre-selection mechanism called visual attention for image and video processing systems would make them more sophisticated and therefore more useful.  ...  Kazuhiko Kojima of Sanyo Electric Co.Ltd, and anonymous associate editors and reviewers for their valuable discussions and useful comments, which helped to improve of this work.  ... 
doi:10.1587/transinf.e96.d.562 fatcat:vkz3cdismbhadgzhickap3tzlm

Image Search With Text Feedback by Visiolinguistic Attention Learning

Yanbei Chen, Shaogang Gong, Loris Bazzani
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
In this work, we tackle this task by a novel Visiolinguistic Attention Learning (VAL) framework.  ...  Specifically, we propose a composite transformer that can be seamlessly plugged in a CNN to selectively preserve and transform the visual features conditioned on language semantics.  ...  Acknowledgement: We would like to thank Maksim Lapin, Michael Donoser, Bojan Pepik, and Sabine Sternig for their helpful discussions.  ... 
doi:10.1109/cvpr42600.2020.00307 dblp:conf/cvpr/ChenGB20 fatcat:zpm32czgujfpvlvxnqhs3tt2re

Cortical dynamics of contextually cued attentive visual learning and search: Spatial and object evidence accumulation

Tsung-Ren Huang, Stephen Grossberg
2010 Psychological review  
How do humans use target-predictive contextual information to facilitate visual search?  ...  How are consistently paired scenic objects and positions learned and used to more efficiently guide search in familiar scenes?  ...  Acknowledgments This work was supported in part by CELEST, a National Science Foundation Science of Learning Center (NSF SBE-0354378) and HRL Laboratories LLC (subcontract #801881-BS under DARPA prime  ... 
doi:10.1037/a0020664 pmid:21038974 fatcat:7okysumkhnh2vncdkbuhgvvmnq

Learning to attend in a brain-inspired deep neural network [article]

Hossein Adeli, Gregory Zelinsky
2018 arXiv   pre-print
Using deep reinforcement learning, ATTNet learned to shift its attention to the visual features of a target category in the context of a search task.  ...  More fundamentally, ATTNet learned to shift its attention to target like objects and spatially route its visual inputs to accomplish the task.  ...  inputs and shifting attention to be a useful thing to do.  ... 
arXiv:1811.09699v1 fatcat:v7fiud7ijzdrlpkx6nspagfvke

Reinforcement learning based visual attention with application to face detection

Ben Goodrich, Itamar Arel
2012 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops  
This paper introduces a novel approach to the problem of visual search by framing it as an adaptive learning process.  ...  The mainstream approach to modeling focal visual attention involves identifying saliencies in the image and applying a search process to the salient regions.  ...  Section II briefly reviews existing visual attention methods. Section III introduces the proposed learning-based approach to the visual search task.  ... 
doi:10.1109/cvprw.2012.6239177 dblp:conf/cvpr/GoodrichA12 fatcat:yf7btrlp4nhahd77psj2ro2w5m
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