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Journal articles on the topic 'Visual attention network'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Sandon, Peter A. "Simulating Visual Attention." Journal of Cognitive Neuroscience 2, no. 3 (1990): 213–31. http://dx.doi.org/10.1162/jocn.1990.2.3.213.

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Selective visual attention serializes the processing of stimulus data to make efficient use of limited processing resources in the human visual system. This paper describes a connectionist network that exhibits a variety of attentional phenomena reported by Treisman, Wolford, Duncan, and others. As demonstrated in several simulations, a hierarchical, multiscale network that uses feature arrays with strong lateral inhibitory connections provides responses in agreement with a number of prominent behaviors associated with visual attention. The overall network design is consistent with a range of

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2

Nieuwenhuis, Sander, and Tobias H. Donner. "The visual attention network untangled." Nature Neuroscience 14, no. 5 (2011): 542–43. http://dx.doi.org/10.1038/nn.2812.

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Knight, Robert T. "Distributed Cortical Network for Visual Attention." Journal of Cognitive Neuroscience 9, no. 1 (1997): 75–91. http://dx.doi.org/10.1162/jocn.1997.9.1.75.

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The contribution of prefrontal and posterior association cortex to voluntary and involuntary visual attention was as sessed using electrophysiological techniques in patients with focal lesions in prefrontal (n = 11), temporal-parietal (n = 10), or lateral parietal cortex (n = 7). Subjects participated in a task requiring detection of designated target stimuli embedded in trains of repetitive stimuli. Infrequent and irrelevant novel visual stimuli were randomly interspersed with the target and background stimuli. Controls generated attention dependent N1 (170 msec) and N2 (243 msec) potentials

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Yang, Yadong, Xiaofeng Wang, Quan Zhao, and Tingting Sui. "Two-Level Attentions and Grouping Attention Convolutional Network for Fine-Grained Image Classification." Applied Sciences 9, no. 9 (2019): 1939. http://dx.doi.org/10.3390/app9091939.

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The focus of fine-grained image classification tasks is to ignore interference information and grasp local features. This challenge is what the visual attention mechanism excels at. Firstly, we have constructed a two-level attention convolutional network, which characterizes the object-level attention and the pixel-level attention. Then, we combine the two kinds of attention through a second-order response transform algorithm. Furthermore, we propose a clustering-based grouping attention model, which implies the part-level attention. The grouping attention method is to stretch all the semantic

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Pipanmekaporn, Luepol, Suwatchai Kamonsantiroj, Chiabwoot Ratanavilisagul, and Sathit Prasomphan. "Spatial Pyramid Attention Enhanced Visual Descriptors for Landmark Retrieval." Journal of Image and Graphics 11, no. 4 (2023): 359–66. http://dx.doi.org/10.18178/joig.11.4.359-366.

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Landmark retrieval, which aims to search for landmark images similar to a query photo within a massive image database, has received considerable attention for many years. Despite this, finding landmarks quickly and accurately still presents some unique challenges. To tackle these challenges, we present a deep learning model, called the Spatial-Pyramid Attention network (SPA). This network is an end-to-end convolutional network, incorporating a spatial-pyramid attention layer that encodes the input image, leveraging the spatial pyramid structure to highlight regional features based on their rel

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Guo, Dan, Hui Wang, Shuhui Wang, and Meng Wang. "Textual-Visual Reference-Aware Attention Network for Visual Dialog." IEEE Transactions on Image Processing 29 (2020): 6655–66. http://dx.doi.org/10.1109/tip.2020.2992888.

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Urbanek, Carsten, Nicholetta Weinges-Evers, Judith Bellmann-Strobl, et al. "Attention Network Test reveals alerting network dysfunction in multiple sclerosis." Multiple Sclerosis Journal 16, no. 1 (2009): 93–99. http://dx.doi.org/10.1177/1352458509350308.

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Attention is one of the cognitive domains typically affected in multiple sclerosis. The Attention Network Test was developed to measure the function of the three distinct attentional networks, alerting, orienting, and executive control. The Attention Network Test has been performed in various neuropsychiatric conditions, but not in multiple sclerosis. Our objective was to investigate functions of attentional networks in multiple sclerosis by means of the Attention Network Test. Patients with relapsing—remitting multiple sclerosis (n = 57) and healthy controls (n = 57) matched for age, sex, and

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Park, Sungjin, Taesun Whang, Yeochan Yoon, and Heuiseok Lim. "Multi-View Attention Network for Visual Dialog." Applied Sciences 11, no. 7 (2021): 3009. http://dx.doi.org/10.3390/app11073009.

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Visual dialog is a challenging vision-language task in which a series of questions visually grounded by a given image are answered. To resolve the visual dialog task, a high-level understanding of various multimodal inputs (e.g., question, dialog history, and image) is required. Specifically, it is necessary for an agent to (1) determine the semantic intent of question and (2) align question-relevant textual and visual contents among heterogeneous modality inputs. In this paper, we propose Multi-View Attention Network (MVAN), which leverages multiple views about heterogeneous inputs based on a

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Yang, Kai, Zhenyu He, Zikun Zhou, and Nana Fan. "SiamAtt: Siamese attention network for visual tracking." Knowledge-Based Systems 203 (September 2020): 106079. http://dx.doi.org/10.1016/j.knosys.2020.106079.

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Du, Wenchao, Hu Chen, Peixi Liao, Hongyu Yang, Ge Wang, and Yi Zhang. "Visual Attention Network for Low-Dose CT." IEEE Signal Processing Letters 26, no. 8 (2019): 1152–56. http://dx.doi.org/10.1109/lsp.2019.2922851.

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Qin, Lin, Yang Yang, Dandan Huang, Naibo Zhu, Han Yang, and Zhisong Xu. "Visual Tracking With Siamese Network Based on Fast Attention Network." IEEE Access 10 (2022): 35632–42. http://dx.doi.org/10.1109/access.2022.3163717.

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Kang, Byeongkeun, and Yeejin Lee. "High-Resolution Neural Network for Driver Visual Attention Prediction." Sensors 20, no. 7 (2020): 2030. http://dx.doi.org/10.3390/s20072030.

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Driving is a task that puts heavy demands on visual information, thereby the human visual system plays a critical role in making proper decisions for safe driving. Understanding a driver’s visual attention and relevant behavior information is a challenging but essential task in advanced driver-assistance systems (ADAS) and efficient autonomous vehicles (AV). Specifically, robust prediction of a driver’s attention from images could be a crucial key to assist intelligent vehicle systems where a self-driving car is required to move safely interacting with the surrounding environment. Thus, in thi

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Spadone, Sara, Stefania Della Penna, Carlo Sestieri, et al. "Dynamic reorganization of human resting-state networks during visuospatial attention." Proceedings of the National Academy of Sciences 112, no. 26 (2015): 8112–17. http://dx.doi.org/10.1073/pnas.1415439112.

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Fundamental problems in neuroscience today are understanding how patterns of ongoing spontaneous activity are modified by task performance and whether/how these intrinsic patterns influence task-evoked activation and behavior. We examined these questions by comparing instantaneous functional connectivity (IFC) and directed functional connectivity (DFC) changes in two networks that are strongly correlated and segregated at rest: the visual (VIS) network and the dorsal attention network (DAN). We measured how IFC and DFC during a visuospatial attention task, which requires dynamic selective rero

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Fu, Jia, Guoming Yu, and Lun Zhao. "Effect of aging on visual attention: Evidence from the Attention Network Test." Social Behavior and Personality: an international journal 49, no. 3 (2021): 1–8. http://dx.doi.org/10.2224/sbp.9806.

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We investigated the effects of aging on attentional functions using the Attention Network Test (ANT), which enables simultaneous testing of alerting, orienting, and executive networks, and their interactions. Participants were 38 young adults (Mage = 21.35 years) and 36 older adults (Mage = 71.17 years). Although the older adults exhibited a slower overall response, the three attentional functions showed different modulation according to age group and the trial block being completed. Older adults exhibited significant impairment in the alerting function, regardless of whether they were complet

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Webb, Taylor W., Kajsa M. Igelström, Aaron Schurger, and Michael S. A. Graziano. "Cortical networks involved in visual awareness independent of visual attention." Proceedings of the National Academy of Sciences 113, no. 48 (2016): 13923–28. http://dx.doi.org/10.1073/pnas.1611505113.

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It is now well established that visual attention, as measured with standard spatial attention tasks, and visual awareness, as measured by report, can be dissociated. It is possible to attend to a stimulus with no reported awareness of the stimulus. We used a behavioral paradigm in which people were aware of a stimulus in one condition and unaware of it in another condition, but the stimulus drew a similar amount of spatial attention in both conditions. The paradigm allowed us to test for brain regions active in association with awareness independent of level of attention. Participants performe

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An, Junkang, and Inwhee Joe. "Attention Map-Guided Visual Explanations for Deep Neural Networks." Applied Sciences 12, no. 8 (2022): 3846. http://dx.doi.org/10.3390/app12083846.

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Deep neural network models perform well in a variety of domains, such as computer vision, recommender systems, natural language processing, and defect detection. In contrast, in areas such as healthcare, finance, and defense, deep neural network models, due to their lack of explainability, are not trusted by users. In this paper, we focus on attention-map-guided visual explanations for deep neural networks. We employ an attention mechanism to find the most important region of an input image. The Grad-CAM method is used to extract the feature map for deep neural networks, and then the attention

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Dong, Feng, Xiaofeng Wang, Ammar Oad, and Mir Sajjad Hussain Talpur. "Co-attention Network for Visual Question Answering Based on Dual Attention." Journal of Engineering Science and Technology Review 14, no. 6 (2021): 116–23. http://dx.doi.org/10.25103/jestr.146.13.

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Zhao, Shuanfeng, Guodong Han, Qingqing Zhao, and Pei Wei. "Prediction of Driver’s Attention Points Based on Attention Model." Applied Sciences 10, no. 3 (2020): 1083. http://dx.doi.org/10.3390/app10031083.

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The current intelligent driving system does not consider the selective attention mechanism of drivers, and it cannot completely replace the drivers to extract effective road information. A Driver Visual Attention Network (DVAN), which is based on deep learning attention model, is proposed in our paper, in order to solve this problem. The DVAN is aimed at extracting the key information affecting the driver’s operation by predicting the driver’s attention points. It completes the fast localization and extraction of road information that is most interesting to drivers by merging local apparent fe

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Kuo, Bo-Cheng, Mark G. Stokes, Alexandra M. Murray, and Anna Christina Nobre. "Attention Biases Visual Activity in Visual Short-term Memory." Journal of Cognitive Neuroscience 26, no. 7 (2014): 1377–89. http://dx.doi.org/10.1162/jocn_a_00577.

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In the current study, we tested whether representations in visual STM (VSTM) can be biased via top–down attentional modulation of visual activity in retinotopically specific locations. We manipulated attention using retrospective cues presented during the retention interval of a VSTM task. Retrospective cues triggered activity in a large-scale network implicated in attentional control and led to retinotopically specific modulation of activity in early visual areas V1–V4. Importantly, shifts of attention during VSTM maintenance were associated with changes in functional connectivity between pFC

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Geetha, S., Mansi Parashar, JS Abhishek, Raj Vishal Turaga, Isah A. Lawal, and Seifedine Kadry. "Diabetic Retinopathy Grading with Deep Visual Attention Network." International Journal of Online and Biomedical Engineering (iJOE) 18, no. 09 (2022): 160–77. http://dx.doi.org/10.3991/ijoe.v18i09.30075.

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Diabetic Retinopathy is a serious complication arising in diabetes afflicted patients. Its effective treatment depends on early detection, and the course of action varies decisively with the intensity of the affliction. Computer-aided diagnosis helps to detect not only the presence or absence of the disease but also the severity, making it easier for ophthalmologists to construct a treatment plan. Diabetic retinopathy grading is the task of classifying images of the eye's fundus of diabetic patients into 5 different grades ranging from 0-4 based on the severity of the disease. In this work, we

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Sun, Xinglong, Haijiang Sun, Shan Jiang, Jiacheng Wang, Xilai Wei, and Zhonghe Hu. "Multi-attention associate prediction network for visual tracking." Neurocomputing 614 (January 2025): 128785. http://dx.doi.org/10.1016/j.neucom.2024.128785.

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Ruwa, Nelson, Qirong Mao, Heping Song, Hongjie Jia, and Ming Dong. "Triple attention network for sentimental visual question answering." Computer Vision and Image Understanding 189 (December 2019): 102829. http://dx.doi.org/10.1016/j.cviu.2019.102829.

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Gao, Long, Yunsong Li, and Jifeng Ning. "Residual Attention Convolutional Network for Online Visual Tracking." IEEE Access 7 (2019): 94097–105. http://dx.doi.org/10.1109/access.2019.2927791.

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Shen, Jianbing, Xin Tang, Xingping Dong, and Ling Shao. "Visual Object Tracking by Hierarchical Attention Siamese Network." IEEE Transactions on Cybernetics 50, no. 7 (2020): 3068–80. http://dx.doi.org/10.1109/tcyb.2019.2936503.

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Mountzouris, Konstantinos, Isidoros Perikos, and Ioannis Hatzilygeroudis. "Speech Emotion Recognition Using Convolutional Neural Networks with Attention Mechanism." Electronics 12, no. 20 (2023): 4376. http://dx.doi.org/10.3390/electronics12204376.

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Speech emotion recognition (SER) is an interesting and difficult problem to handle. In this paper, we deal with it through the implementation of deep learning networks. We have designed and implemented six different deep learning networks, a deep belief network (DBN), a simple deep neural network (SDNN), an LSTM network (LSTM), an LSTM network with the addition of an attention mechanism (LSTM-ATN), a convolutional neural network (CNN), and a convolutional neural network with the addition of an attention mechanism (CNN-ATN), having in mind, apart from solving the SER problem, to test the impact

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Li, Muhua, and James J. Clark. "A Temporal Stability Approach to Position and Attention-Shift-Invariant Recognition." Neural Computation 16, no. 11 (2004): 2293–321. http://dx.doi.org/10.1162/0899766041941907.

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Incorporation of visual-related self-action signals can help neural networks learn invariance. We describe a method that can produce a network with invariance to changes in visual input caused by eye movements and covert attention shifts. Training of the network is controlled by signals associated with eye movements and covert attention shifting. A temporal perceptual stability constraint is used to drive the output of the network toward remaining constant across temporal sequences of saccadicmotions and covert attention shifts. We use a four-layer neural network model to perform the position-

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Song, Bofan, Chicheng Zhang, Sumsum Sunny, et al. "Interpretable and Reliable Oral Cancer Classifier with Attention Mechanism and Expert Knowledge Embedding via Attention Map." Cancers 15, no. 5 (2023): 1421. http://dx.doi.org/10.3390/cancers15051421.

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Convolutional neural networks have demonstrated excellent performance in oral cancer detection and classification. However, the end-to-end learning strategy makes CNNs hard to interpret, and it can be challenging to fully understand the decision-making procedure. Additionally, reliability is also a significant challenge for CNN based approaches. In this study, we proposed a neural network called the attention branch network (ABN), which combines the visual explanation and attention mechanisms to improve the recognition performance and interpret the decision-making simultaneously. We also embed

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Truong, Quoc-Tuan, and Hady W. Lauw. "VistaNet: Visual Aspect Attention Network for Multimodal Sentiment Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 305–12. http://dx.doi.org/10.1609/aaai.v33i01.3301305.

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Detecting the sentiment expressed by a document is a key task for many applications, e.g., modeling user preferences, monitoring consumer behaviors, assessing product quality. Traditionally, the sentiment analysis task primarily relies on textual content. Fueled by the rise of mobile phones that are often the only cameras on hand, documents on the Web (e.g., reviews, blog posts, tweets) are increasingly multimodal in nature, with photos in addition to textual content. A question arises whether the visual component could be useful for sentiment analysis as well. In this work, we propose Visual

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Wang, Tianwei, Yuanzhi Zhu, Lianwen Jin, et al. "Decoupled Attention Network for Text Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 12216–24. http://dx.doi.org/10.1609/aaai.v34i07.6903.

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Text recognition has attracted considerable research interests because of its various applications. The cutting-edge text recognition methods are based on attention mechanisms. However, most of attention methods usually suffer from serious alignment problem due to its recurrency alignment operation, where the alignment relies on historical decoding results. To remedy this issue, we propose a decoupled attention network (DAN), which decouples the alignment operation from using historical decoding results. DAN is an effective, flexible and robust end-to-end text recognizer, which consists of thr

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Lin, Haixi, Qingxia Lin, Hailong Li, et al. "Functional Connectivity of Attention-Related Networks in Drug-Naïve Children With ADHD." Journal of Attention Disorders 25, no. 3 (2018): 377–88. http://dx.doi.org/10.1177/1087054718802017.

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Objective: This study aimed to explore alterations of seed-based functional connectivity (FC) in dorsal attention network (DAN), ventral attention network (VAN), and default mode network (DMN) in ADHD children. Method: A voxel-based comparison of FC maps between 46 drug-naïve children with ADHD and 31 healthy controls (HCs) and correlation analysis between connectivity features and behavior were performed. Results: Compared with the HCs, children with ADHD were characterized by hyperconnectivity between DAN and regions of DMN and by hyperconnectivity between DMN and a set of regions involved i

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Xiang, Yingxin, Chengyuan Zhang, Zhichao Han, Hao Yu, Jiaye Li, and Lei Zhu. "Path-Wise Attention Memory Network for Visual Question Answering." Mathematics 10, no. 18 (2022): 3244. http://dx.doi.org/10.3390/math10183244.

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Visual question answering (VQA) is regarded as a multi-modal fine-grained feature fusion task, which requires the construction of multi-level and omnidirectional relations between nodes. One main solution is the composite attention model which is composed of co-attention (CA) and self-attention(SA). However, the existing composite models only consider the stack of single attention blocks, lack of path-wise historical memory, and overall adjustments. We propose a path attention memory network (PAM) to construct a more robust composite attention model. After each single-hop attention block (SA o

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Yan, Feng, Wushouer Silamu, and Yanbing Li. "Deep Modular Bilinear Attention Network for Visual Question Answering." Sensors 22, no. 3 (2022): 1045. http://dx.doi.org/10.3390/s22031045.

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VQA (Visual Question Answering) is a multi-model task. Given a picture and a question related to the image, it will determine the correct answer. The attention mechanism has become a de facto component of almost all VQA models. Most recent VQA approaches use dot-product to calculate the intra-modality and inter-modality attention between visual and language features. In this paper, the BAN (Bilinear Attention Network) method was used to calculate attention. We propose a deep multimodality bilinear attention network (DMBA-NET) framework with two basic attention units (BAN-GA and BAN-SA) to cons

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Mahoney, Jeannette R., Joe Verghese, Kristina Dumas, Cuiling Wang, and Roee Holtzer. "Multisensory cueing and the attention network test in aging." Seeing and Perceiving 25 (2012): 20. http://dx.doi.org/10.1163/187847612x646424.

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The Attention Network Test (ANT) assesses the effect of alerting and orienting cues on a visual flanker task measuring executive attention. Previous findings revealed that older adults demonstrate greater RT benefits when provided with visual orienting cues that offer both spatial and temporal information of an ensuing target. Given the overlap of neural correlates involved in multisensory processing and cueing (i.e., alerting and orienting), especially in the superior colliculus, thalamus, superior temporal and parietal regions, an investigation of multisensory cueing effects was warranted. T

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Jiang, Zhaoyin, Shucheng Huang, and Mingxing Li. "A Pedestrian Detection Network Based on an Attention Mechanism and Pose Information." Applied Sciences 14, no. 18 (2024): 8214. http://dx.doi.org/10.3390/app14188214.

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Pedestrian detection has recently attracted widespread attention as a challenging problem in computer vision. The accuracy of pedestrian detection is affected by differences in gestures, background clutter, local occlusion, differences in scales, pixel blur, and other factors occurring in real scenes. These problems lead to false and missed detections. In view of these visual description deficiencies, we leveraged pedestrian pose information as a supplementary resource to address the occlusion challenges that arise in pedestrian detection. An attention mechanism was integrated into the visual

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Li, Te, Fang Yang, and Yao Song. "Visual Attention Adversarial Networks for Chinese Font Translation." Electronics 12, no. 6 (2023): 1388. http://dx.doi.org/10.3390/electronics12061388.

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Currently, many Chinese font translation models adopt the method of dividing character components to improve the quality of generated font images. However, character components require a large amount of manual annotation to decompose characters and determine the composition of each character as input for training. In this paper, we establish a Chinese font translation model based on generative adversarial network without decomposition. First, we improve the method of image enhancement for Chinese character images. It helps the model learning structure information of Chinese character strokes t

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DENG, Zhen, Yi-bin WANG, and Li-bo LIU. "Attentive residual dense network of visual attention mechanism for weakly illuminated image enhancement." Chinese Journal of Liquid Crystals and Displays 36, no. 11 (2021): 1463–73. http://dx.doi.org/10.37188/cjlcd.2021-0098.

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Corbetta, Maurizio, and Gordon L. Shulman. "Human cortical mechanisms of visual attention during orienting and search." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 353, no. 1373 (1998): 1353–62. http://dx.doi.org/10.1098/rstb.1998.0289.

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Functional anatomical studies indicate that a set of neural signals in parietal and frontal cortex mediates the covert allocation of attention to visual locations across a wide variety of visual tasks. This fronto–parietal network includes areas, such as the frontal eye field and supplementary eye field. This anatomical overlap suggests that shifts of attention to visual locations or objects recruit areas involved in oculomotor programming and execution. Finally, the fronto–parietal network may be the source of spatial attentional modulations in the ventral visual system during object recognit

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Diao, Zhifeng, and Fanglei Sun. "Visual Object Tracking Based on Deep Neural Network." Mathematical Problems in Engineering 2022 (July 12, 2022): 1–9. http://dx.doi.org/10.1155/2022/2154463.

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Computer vision systems cannot function without visual target tracking. Intelligent video monitoring, medical treatment, human-computer interaction, and traffic management all stand to benefit greatly from this technology. Although many new algorithms and methods emerge every year, the reality is complex. Targets are often disturbed by factors such as occlusion, illumination changes, deformation, and rapid motion. Solving these problems has also become the main task of visual target tracking researchers. As with the development for deep neural networks and attention mechanisms, object-tracking

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Guan, Zhongtian, Meng Lin, Qiong Wu, et al. "Neural mechanisms of top-down divided and selective spatial attention in visual and auditory perception." Brain Science Advances 9, no. 2 (2023): 95–113. http://dx.doi.org/10.26599/bsa.2023.9050008.

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Top-down attention mechanisms require the selection of specific objects or locations; however, the brain mechanism involved when attention is allocated across different modalities is not well understood. The aim of this study was to use functional magnetic resonance imaging to define the neural mechanisms underlying divided and selective spatial attention. A concurrent audiovisual stimulus was used, and subjects were prompted to focus on a visual, auditory and audiovisual stimulus in a Posner paradigm. Our behavioral results confirmed the better performance of selective attention compared to d

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Li, Jiafeng, Kang Zhang, Zheng Gao, Liheng Yang, and Li Zhuo. "SiamPRA: An Effective Network for UAV Visual Tracking." Electronics 12, no. 11 (2023): 2374. http://dx.doi.org/10.3390/electronics12112374.

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The visual navigation system is an important module in intelligent unmanned aerial vehicle (UAV) systems as it helps to guide them autonomously by tracking visual targets. In recent years, tracking algorithms based on Siamese networks have demonstrated outstanding performance. However, their application to UAV systems has been challenging due to the limited resources available in such systems.This paper proposes a simple and efficient tracking network called the Siamese Pruned ResNet Attention (SiamPRA) network and applied to embedded platforms that can be deployed on UAVs. SiamPRA is base on

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Kristensen, Terje. "Towards Spike based Models of Visual Attention in the Brain." International Journal of Adaptive, Resilient and Autonomic Systems 6, no. 2 (2015): 117–38. http://dx.doi.org/10.4018/ijaras.2015070106.

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A numerical solution of Hodgkin Huxley equations is presented to simulate the spiking behavior of a biological neuron. The solution is illustrated by building a graphical chart interface to finely tune the behavior of the neuron under different stimulations. In addition, a Multi-Agent System (MAS) has been developed to simulate the Visual Attention Network Model of the brain. Tasks are assigned to the agents according to the Attention Network Theory, developed by neuroscientists. A sequential communication model based on simple objects has been constructed, aiming to show the relations and the

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Jiang, Weitao, Weixuan Wang, and Haifeng Hu. "Bi-Directional Co-Attention Network for Image Captioning." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 4 (2021): 1–20. http://dx.doi.org/10.1145/3460474.

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Image Captioning, which automatically describes an image with natural language, is regarded as a fundamental challenge in computer vision. In recent years, significant advance has been made in image captioning through improving attention mechanism. However, most existing methods construct attention mechanisms based on singular visual features, such as patch features or object features, which limits the accuracy of generated captions. In this article, we propose a Bidirectional Co-Attention Network (BCAN) that combines multiple visual features to provide information from different aspects. Diff

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Chen, Chongqing, Dezhi Han, and Chin-Chen Chang. "CAAN: Context-Aware attention network for visual question answering." Pattern Recognition 132 (December 2022): 108980. http://dx.doi.org/10.1016/j.patcog.2022.108980.

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Wang, Jiangtao, Songwei Wang, Zhizhong Wang, Xiaoke Niu, and Li Shi. "Visual Stimulus-Specific Adaptation in Midbrain Selective Attention Network." International Journal of Psychophysiology 168 (October 2021): S218. http://dx.doi.org/10.1016/j.ijpsycho.2021.07.588.

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Zhou, Kang, Chi Guo, and Huyin Zhang. "Relational attention-based Markov logic network for visual navigation." Journal of Supercomputing 78, no. 7 (2022): 9907–33. http://dx.doi.org/10.1007/s11227-021-04283-5.

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Fan, Hehe, Linchao Zhu, Yi Yang, and Fei Wu. "Recurrent Attention Network with Reinforced Generator for Visual Dialog." ACM Transactions on Multimedia Computing, Communications, and Applications 16, no. 3 (2020): 1–16. http://dx.doi.org/10.1145/3390891.

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Fichtenholtz, Harlan M., Heather L. Dean, Daniel G. Dillon, Hiroshi Yamasaki, Gregory McCarthy, and Kevin S. LaBar. "Emotion–attention network interactions during a visual oddball task." Cognitive Brain Research 20, no. 1 (2004): 67–80. http://dx.doi.org/10.1016/j.cogbrainres.2004.01.006.

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Rosen, Maya L., Chantal E. Stern, Samantha W. Michalka, Kathryn J. Devaney, and David C. Somers. "Cognitive Control Network Contributions to Memory-Guided Visual Attention." Cerebral Cortex 26, no. 5 (2015): 2059–73. http://dx.doi.org/10.1093/cercor/bhv028.

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Wang, Fan, Bo Yang, Jingting Li, Xiaopeng Hu, and Zhihang Ji. "Attention-Based Siamese Region Proposals Network for Visual Tracking." IEEE Access 8 (2020): 86595–607. http://dx.doi.org/10.1109/access.2020.2991238.

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Kim, Yeongbin, Joongchol Shin, Hasil Park, and Joonki Paik. "Real-Time Visual Tracking with Variational Structure Attention Network." Sensors 19, no. 22 (2019): 4904. http://dx.doi.org/10.3390/s19224904.

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Abstract:

Online training framework based on discriminative correlation filters for visual tracking has recently shown significant improvement in both accuracy and speed. However, correlation filter-base discriminative approaches have a common problem of tracking performance degradation when the local structure of a target is distorted by the boundary effect problem. The shape distortion of the target is mainly caused by the circulant structure in the Fourier domain processing, and it makes the correlation filter learn distorted training samples. In this paper, we present a structure–attention network t

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