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Imaging attention networks
2012
NeuroImage
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The presence of a functional anatomy has supported studies of the development of attention networks and the role of neuromodulators and genetic polymorphisms in their construction. ...
evidence of brain networks related to orienting to sensory events and control of response tendencies. ...
Education Imaging has begun to be applied to training of attentional networks of children prior to starting school. ...
doi:10.1016/j.neuroimage.2011.12.040
pmid:22227132
pmcid:PMC3345293
fatcat:3zg6wos5bfdsbfogxw4jvhooy4
Attention in Attention Network for Image Super-Resolution
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2104.09497v2 fatcat:6yxcrs2csrg2jltsaevowr2hk4Other Versions
Convolutional neural networks have allowed remarkable advances in single image super-resolution (SISR) over the last decade. ...
We then propose attention in attention network (A^2N) for more efficient and accurate SISR. Specifically, A^2N consists of a non-attention branch and a coupling attention branch. ...
Conclusions In this work, we propose attention in attention networks (A 2 N) and building block A 2 B for image SR. ...
arXiv:2104.09497v3
fatcat:becpyjjjgjdgndxwf45h6zmtgu
Unsupervised Image-to-Image Translation with Self-Attention Networks
[article]
2019
arXiv
pre-print
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However, the effectiveness of the self-attention network in unsupervised image-to-image translation tasks have not been verified. ...
In this paper, we propose an unsupervised image-to-image translation with self-attention networks, in which long range dependency helps to not only capture strong geometric change but also generate details ...
Methods
Unpaired Image-to-Image Translation with Self Attention Networks We propose an unsupervised image-to-image translation model with self-attention networks that allows long range dependency modeling ...
arXiv:1901.08242v3
fatcat:2fw2oclkyzaobjzstr2xd5s5ke
Attention Cube Network for Image Restoration
[article]
2020
arXiv
pre-print
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To address these issues, we propose an attention cube network (A-CubeNet) for image restoration for more powerful feature expression and feature correlation learning. ...
Recently, deep convolutional neural network (CNN) have been widely used in image restoration and obtained great success. ...
In order to solve above problems, this paper proposes an attention cube network (A-CubeNet) for image restoration based on adaptive dual attention module (ADAM) and adaptive hierarchical attention module ...
arXiv:2009.05907v2
fatcat:keguyfstkfe5tl7lus3tmjr5uu
Saliency-Guided Attention Network for Image-Sentence Matching
[article]
2021
arXiv
pre-print
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2019 pre-print arXiv:1904.09471v3 fatcat:ggmpgfdvivdjbgv26gic3bbhk4Other Versions
Unlike previous approaches that predominantly deploy symmetrical architecture to represent both modalities, we propose Saliency-guided Attention Network (SAN) that asymmetrically employs visual and textual ...
This paper studies the task of matching image and sentence, where learning appropriate representations across the multi-modal data appears to be the main challenge. ...
The conceptual diagram of Saliency-guided Attention Network (SAN) for image-sentence matching. ...
arXiv:1904.09471v4
fatcat:zmukwaqu2ja5do7mgr3cxev3yi
Residual Attention Network for Image Classification
[article]
2017
arXiv
pre-print
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In this work, we propose "Residual Attention Network", a convolutional neural network using attention mechanism which can incorporate with state-of-art feed forward network architecture in an end-to-end ...
Our Residual Attention Network is built by stacking Attention Modules which generate attention-aware features. ...
Our Residual Attention Network outperforms state-of-the-art residual networks on CIFAR-10, CIFAR-100 and challenging ImageNet [5] image classification dataset with significant reduction of computation ...
arXiv:1704.06904v1
fatcat:zwz5li237bgrfpmgwyzgthyiry
Deliberate Attention Networks for Image Captioning
2019
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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In this paper, we present a novel Deliberate Residual Attention Network, namely DA, for image captioning. ...
To date, encoder-decoder framework with attention mechanisms has achieved great progress for image captioning. ...
Attention Network for image captioning. ...
doi:10.1609/aaai.v33i01.33018320
fatcat:nmdpntbkfjguxgql4iy7qd3d3e
Channel Attention Networks for Image Translation
2019
IEEE Access
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To prevent these problems, we propose the channel attention networks for image translation in this paper. ...
Conditioning on the target domain label, an auto-encoder-like network with multiple attention connections is trained to translate the input image into the target domain. ...
The contribution of our paper is many-fold: • We introduce a channel attention network for image translation. ...
doi:10.1109/access.2019.2926882
fatcat:tstb2ftzcrfcnkt3yfr3fsfldy
Pyramid Attention Networks for Image Restoration
[article]
2020
arXiv
pre-print
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Our code will be available at https://github.com/SHI-Labs/Pyramid-Attention-Networks ...
However, recent advanced deep convolutional neural network based methods for image restoration do not take full advantage of self-similarities by relying on self-attention neural modules that only process ...
We inset a single pyramid attention in the middle of the network. ...
arXiv:2004.13824v4
fatcat:4tq7ea4ntfdvhazjeqxa3zn7ye
AttentionGAN: Unpaired Image-to-Image Translation using Attention-Guided Generative Adversarial Networks
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:1911.11897v4 fatcat:lupc4hdp6bbofoyhmv2dti75geOther Versions
In this paper, we propose a new Attention-Guided Generative Adversarial Networks (AttentionGAN) for the unpaired image-to-image translation task. ...
The attention-guided generators in AttentionGAN are able to produce attention masks, and then fuse the generation output with the attention masks to obtain high-quality target images. ...
[8] use an extra attention network to generate attention maps, so that more attention can be paid to objects of interests. ...
arXiv:1911.11897v5
fatcat:zx2qbwdhnraj7krnrv2xaghhqi
Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image Translation
[article]
2019
arXiv
pre-print
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The state-of-the-art approaches in Generative Adversarial Networks (GANs) are able to learn a mapping function from one image domain to another with unpaired image data. ...
The attention-guided generators in AGGAN are able to produce attention masks via a built-in attention mechanism, and then fuse the input image with the attention mask to obtain a target image with high-quality ...
[12] present ATAGAN, which use a teacher network to produce attention maps. Zhang et al. [45] propose the Self-Attention Generative Adversarial Networks (SAGAN) for image generation task. ...
arXiv:1903.12296v3
fatcat:xcsgwhsf6nfmppgjvrjdg2fwga
Augmented Equivariant Attention Networks for Microscopy Image Reconstruction
[article]
2021
arXiv
pre-print
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To address these limitations, we propose the augmented equivariant attention networks (AEANets) with better capability to capture inter-image dependencies, while preserving the equivariance property. ...
The proposed AEANets captures inter-image dependencies and shared features via two augmentations on the attention mechanism, which are the shared references and the batch-aware attention during training ...
Hovy, “Hierarchical attention networks for docu- Y. ...
arXiv:2011.03633v3
fatcat:r7t5ukhgmvhcfhbl7tnavvheuq
Attention Guided Network for Retinal Image Segmentation
[article]
2019
arXiv
pre-print
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In this paper, we propose an Attention Guided Network (AG-Net) to preserve the structural information and guide the expanding operation. ...
Learning structural information is critical for producing an ideal result in retinal image segmentation. ...
Finally, we propose Attention Guided Network (AG-Net) to preserve the structural information and guide the expanding operation. ...
arXiv:1907.12930v3
fatcat:gr33xlutbfazpmvmjoan7zass4
AWNet: Attentive Wavelet Network for Image ISP
[article]
2020
arXiv
pre-print
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In this paper, we introduce a novel network that utilizes the attention mechanism and wavelet transform, dubbed AWNet, to tackle this learnable image ISP problem. ...
Owing to the rapid rise of deep learning, recent works resort to the deep convolutional neural network (CNN) to develop a sophisticated data-driven ISP that directly maps the phone-captured image to the ...
By considering that the convolutional kernel only covers the local information of an image, [34] proposed a non-local attention mechanism. ...
arXiv:2008.09228v2
fatcat:zeesafdqxbhkvnpyjqgcrxw4mi
Stacked Attention Networks for Image Question Answering
2016
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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This paper presents stacked attention networks (SANs) that learn to answer natural language questions from images. ...
The visualization of the attention layers illustrates the progress that the SAN locates the relevant visual clues that lead to the answer of the question layer-by-layer. ...
First, we propose a stacked attention network for image QA tasks. ...
doi:10.1109/cvpr.2016.10
dblp:conf/cvpr/YangHGDS16
fatcat:l4t5xkddnjcrlnxn6elqhzzbcy
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