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Imaging attention networks

Michael I. Posner
2012 NeuroImage  
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]

Haoyu Chen, Jinjin Gu, Zhi Zhang
2021 arXiv   pre-print
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]

Taewon Kang, Kwang Hee Lee
2019 arXiv   pre-print
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]

Yucheng Hang, Qingmin Liao, Wenming Yang, Yupeng Chen, Jie Zhou
2020 arXiv   pre-print
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]

Zhong Ji, Haoran Wang, Jungong Han, Yanwei Pang
2021 arXiv   pre-print
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]

Fei Wang, Mengqing Jiang, Chen Qian, Shuo Yang, Cheng Li, Honggang Zhang, Xiaogang Wang, Xiaoou Tang
2017 arXiv   pre-print
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

Lianli Gao, Kaixuan Fan, Jingkuan Song, Xianglong Liu, Xing Xu, Heng Tao Shen
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
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

Song Sun, Bo Zhao, Xin Chen, Muhammad Mateen, Junhao Wen
2019 IEEE Access  
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]

Yiqun Mei, Yuchen Fan, Yulun Zhang, Jiahui Yu, Yuqian Zhou, Ding Liu, Yun Fu, Thomas S. Huang, Humphrey Shi
2020 arXiv   pre-print
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]

Hao Tang and Hong Liu and Dan Xu and Philip H.S. Torr and Nicu Sebe
2021 arXiv   pre-print
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]

Hao Tang, Dan Xu, Nicu Sebe, Yan Yan
2019 arXiv   pre-print
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]

Yaochen Xie, Yu Ding, Shuiwang Ji
2021 arXiv   pre-print
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]

Shihao Zhang, Huazhu Fu, Yuguang Yan, Yubing Zhang, Qingyao Wu, Ming Yang, Mingkui Tan, Yanwu Xu
2019 arXiv   pre-print
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]

Linhui Dai, Xiaohong Liu, Chengqi Li, Jun Chen
2020 arXiv   pre-print
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

Zichao Yang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Smola
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
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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