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Статті в журналах з теми "Attention aware"
Wang, Zhibo, Jinxin Ma, Yongquan Zhang, Qian Wang, Ju Ren, and Peng Sun. "Attention-over-Attention Field-Aware Factorization Machine." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 6323–30. http://dx.doi.org/10.1609/aaai.v34i04.6101.
Повний текст джерелаYang, Baosong, Jian Li, Derek F. Wong, Lidia S. Chao, Xing Wang, and Zhaopeng Tu. "Context-Aware Self-Attention Networks." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 387–94. http://dx.doi.org/10.1609/aaai.v33i01.3301387.
Повний текст джерелаVertegaal, Roel, and Jeffrey S. Shell. "Attentive user interfaces: the surveillance and sousveillance of gaze-aware objects." Social Science Information 47, no. 3 (September 2008): 275–98. http://dx.doi.org/10.1177/0539018408092574.
Повний текст джерелаWu, Haiping, Khimya Khetarpal, and Doina Precup. "Self-Supervised Attention-Aware Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (May 18, 2021): 10311–19. http://dx.doi.org/10.1609/aaai.v35i12.17235.
Повний текст джерелаJian, Muwei, Kin-Man Lam, Junyu Dong, and Linlin Shen. "Visual-Patch-Attention-Aware Saliency Detection." IEEE Transactions on Cybernetics 45, no. 8 (August 2015): 1575–86. http://dx.doi.org/10.1109/tcyb.2014.2356200.
Повний текст джерелаMo, Rongyun, Shenqi Lai, Yan Yan, Zhenhua Chai, and Xiaolin Wei. "Dimension-aware attention for efficient mobile networks." Pattern Recognition 131 (November 2022): 108899. http://dx.doi.org/10.1016/j.patcog.2022.108899.
Повний текст джерелаSiragusa, Giovanni, and Livio Robaldo. "Sentence Graph Attention For Content-Aware Summarization." Applied Sciences 12, no. 20 (October 14, 2022): 10382. http://dx.doi.org/10.3390/app122010382.
Повний текст джерелаSong, Junyu, Kaifang Li, Guancheng Hui, and Miaohui Zhang. "Relation Aware Attention for Penson Re-identification." Journal of Physics: Conference Series 2010, no. 1 (September 1, 2021): 012130. http://dx.doi.org/10.1088/1742-6596/2010/1/012130.
Повний текст джерелаLyu, Kejie, Yingming Li, and Zhongfei Zhang. "Attention-Aware Multi-Task Convolutional Neural Networks." IEEE Transactions on Image Processing 29 (2020): 1867–78. http://dx.doi.org/10.1109/tip.2019.2944522.
Повний текст джерелаCelikcan, Ufuk, Gokcen Cimen, E. Bengu Kevinc, and Tolga Capin. "Attention-Aware Disparity Control in interactive environments." Visual Computer 29, no. 6-8 (April 26, 2013): 685–94. http://dx.doi.org/10.1007/s00371-013-0804-6.
Повний текст джерелаДисертації з теми "Attention aware"
Larsson, Joakim. "Using gaze aware regions in eye tracking calibration for users with low-attention span." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-216672.
Повний текст джерелаÖgonstyrning har under en längre tid möjliggjort interaktion för användare. Dock är det fortfarande många utmaningar för att göra interaktionen lätt för användare med intellektuella funktionsnedsättningar. Framförallt när det kommer till inställningar för ögonstyrning, där kalibrering har visat sig vara viktigt för att ge en noggrann uppskattning vart användarna fokuserar. Denna rapport presenterar en studie där tre modifierade versioner av ett kalibreringsgränsnitt för ögonstyrning har blivit designat och utvärderat av nio deltagare med låg fokuseringsförmåga. Dessa gränssnitt använde regioner som var medvetna när en användare tittade inom dom, så kallade blickmedvetna regioner, och varierade i vilken hastighet ett stimuli rörde sig och hur snabbt regionerna runt ett stimuli växte. Data samlades in för varje gränssnitt om interaktionen med de blickmedvetna regionerna, tiden för att genomföra kalibreringen, antal avklarade kalibreringspunkter och avståndet mellan användarnas blick och stimuli. Ingen statistisk signifikans hittades mellan de modifierade gränssnitten mellan tidseffektivitet, effektivitet och noggrannhet. Däremot indikerades en mer tidseffektiv och effektiv kalibreringsmetod, utan minskad noggrannhet, genom användningen av ett stimuli som rör sig snabbare med blickmedvetna regioner som växer. Dessutom skulle kalibreringsprocessen kunna förbättras om enbart engagemang med skärmen används genom smooth-pursuit kalibrering
Toyama, Takumi [Verfasser], Andreas [Akademischer Betreuer] Dengel, and Marcus [Akademischer Betreuer] Eichenberger-Liwicki. "Towards wearable attention-aware systems in everyday environments / Takumi Toyama. Betreuer: Andreas Dengel ; Marcus Eichenberger-Liwicki." Kaiserslautern : Technische Universität Kaiserslautern, 2015. http://d-nb.info/1078898391/34.
Повний текст джерелаPaulin, Rémi. "human-robot motion : an attention-based approach." Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAM018.
Повний текст джерелаFor autonomous mobile robots designed to share their environment with humans, path safety and efficiency are not the only aspects guiding their motion: they must follow social rules so as not to cause discomfort to surrounding people. Most socially-aware path planners rely heavily on the concept of social spaces; however, social spaces are hard to model and they are of limited use in the context of human-robot interaction where intrusion into social spaces is necessary. In this work, a new approach for socially-aware path planning is presented that performs well in complex environments as well as in the context of human-robot interaction. Specifically, the concept of attention is used to model how the influence of the environment as a whole affects how the robot's motion is perceived by people within close proximity. A new computational model of attention is presented that estimates how our attentional resources are shared amongst the salient elements in our environment. Based on this model, the novel concept of attention field is introduced and a path planner that relies on this field is developed in order to produce socially acceptable paths. To do so, a state-of-the-art many-objective optimization algorithm is successfully applied to the path planning problem. The capacities of the proposed approach are illustrated in several case studies where the robot is assigned different tasks. Firstly, when the task is to navigate in the environment without causing distraction our approach produces promising results even in complex situations. Secondly, when the task is to attract a person's attention in view of interacting with him or her, the motion planner is able to automatically choose a destination that best conveys its desire to interact whilst keeping the motion safe, efficient and socially acceptable
Nagao, Katashi, Kazutoshi Kozakai, Meguru Ito, Issei Naruta, and Shigeki Ohira. "Attentive Townvehicle Environment-Aware Personal Intelligent Vehicles." INTELLIGENT MEDIA INTEGRATION NAGOYA UNIVERSITY / COE, 2005. http://hdl.handle.net/2237/10371.
Повний текст джерелаShang, Guokan. "Spoken Language Understanding for Abstractive Meeting Summarization Unsupervised Abstractive Meeting Summarization with Multi-Sentence Compression and Budgeted Submodular Maximization. Energy-based Self-attentive Learning of Abstractive Communities for Spoken Language Understanding Speaker-change Aware CRF for Dialogue Act Classification." Thesis, Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAX011.
Повний текст джерелаWith the impressive progress that has been made in transcribing spoken language, it is becoming increasingly possible to exploit transcribed data for tasks that require comprehension of what is said in a conversation. The work in this dissertation, carried out in the context of a project devoted to the development of a meeting assistant, contributes to ongoing efforts to teach machines to understand multi-party meeting speech. We have focused on the challenge of automatically generating abstractive meeting summaries.We first present our results on Abstractive Meeting Summarization (AMS), which aims to take a meeting transcription as input and produce an abstractive summary as output. We introduce a fully unsupervised framework for this task based on multi-sentence compression and budgeted submodular maximization. We also leverage recent advances in word embeddings and graph degeneracy applied to NLP, to take exterior semantic knowledge into account and to design custom diversity and informativeness measures.Next, we discuss our work on Dialogue Act Classification (DAC), whose goal is to assign each utterance in a discourse a label that represents its communicative intention. DAC yields annotations that are useful for a wide variety of tasks, including AMS. We propose a modified neural Conditional Random Field (CRF) layer that takes into account not only the sequence of utterances in a discourse, but also speaker information and in particular, whether there has been a change of speaker from one utterance to the next.The third part of the dissertation focuses on Abstractive Community Detection (ACD), a sub-task of AMS, in which utterances in a conversation are grouped according to whether they can be jointly summarized by a common abstractive sentence. We provide a novel approach to ACD in which we first introduce a neural contextual utterance encoder featuring three types of self-attention mechanisms and then train it using the siamese and triplet energy-based meta-architectures. We further propose a general sampling scheme that enables the triplet architecture to capture subtle patterns (e.g., overlapping and nested clusters)
Delion, Matthieu. "La chirurgie éveillée chez l'enfant Specifities of awake craniotomy and brain mapping in children for resection of supratentorial tumours in the language areas." Thesis, Angers, 2016. http://www.theses.fr/2016ANGE0073.
Повний текст джерелаIntraoperative cortical and subcortical direct stimulation surgery while awake (CSCSSA) is rarely used to operate in functional areas of the brain in children. Only small series have been published regarding children. However, this procedure is considered to be a gold standard for identifying and preserving the eloquent cortical and subcortical sites. Indeed the child’s survival and the quality of life depend on the quality of the tumor resection. The unifying idea of my thesis was the transfer of the CSCSSA from adults to children.The first work of this thesis was to study the feasibility of the CSCSSA in children through our clinical experience. We also showed that CSCSSA could be applied in children in a safe way with good clinical and radiological results. Some precautions should also be observed, notably concerning the preparation of these young patients. The second step of this thesis was to evaluate the psychological impact of this kind of procedure in children, thanks to the cooperation of the child psychiatrists. The child’s experience was good in every case. None of our patients presented symptoms of post-traumatic stress disorder after the surgery. The third objective was to evaluate the use of resting state functional MRI (rsfMRI) in children for the preoperative planning. We demonstrated not only the strong correlation between rsfMRI and brain electrical mapping, but also the superiority in terms of sensibility and specificity of rsfMRI compared to task based functional MRI. Indeed rsfMRI allowed us to isolate the attentional networks, which interfere with the results of task based functional MRI
Lin, Yu-Shan, and 林雨姍. "Attention-Aware Interactive Display Wall." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/58256298930039666233.
Повний текст джерела國立臺灣大學
資訊網路與多媒體研究所
101
We can find many digital signs in both public and private environments. Digital signs frequently display content by utilizing technologies such as LCD, LED, plasma displays, or projected images. Digital signage can extend the interactive abilities through the accompanying employment. In our work, we use two projectors to project a high resolution image onto a projection screen, and set a camera in the middle of the screen. We want to know where the viewer is looking at in the image, i.e., the focus of attention of viewer. Because of some limitation of gaze estimation, we use head pose estimation to estimate human attention. Besides, we also conduct some experiments to find out the bias estimating human attention by using head pose only. In applications, we show respect to Andy Warhol, leading figure in Pop Art, and design an interactive wall, which consists of 4x11 arrays of small screens presenting the continuous metamorphosis of “portraits.” When a participant is attracted by the brilliant variation of the portraits and sits on the chair with pressure sensors, his/her face will be captured into the system and activate a series of interactive activities driven by his/her attention. To fulfill the work, several technical components are integrated, including image processing, head pose estimation, pressure detection, and etc. Since interaction driven by human attention is the most intuitive approach, participants can easily engage in the interactive art. After several times of public demonstrations, we have acquired many precious feedbacks from user-experience questionnaires. We also proposed some approaches to improve user experience.
Chandu, Chiranjeevi. "Region of Interest Aware and Impairment Based Image Quality Assessment." Thesis, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13662.
Повний текст джерелаHsu, Fang-Chin, and 徐芳秦. "Spatial-Aware Channel Attention Mechanism and Temporal Embedding Propagation Module Based on Deep Learning Architecture for Real-time Visual Object Tracking System." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/t2vajv.
Повний текст джерела國立臺灣科技大學
電機工程系
107
This study proposed a spatial-aware channel attention mechanism and temporal embedding propagation module based on deep learning architecture for real-time visual object tracking system. The proposed method is an advanced research based on the SiamRPN, and it is an end-to-end off-line trained network, and can provide real-time efficiency. For the proposed spatial-aware channel attention mechanism, we apply it to reweight the channels while extracting the template feature and enhance the generalization power of the model. The system can benefit from the mechanism and find the representative feature which can adapt to the time-varied appearance of an object or background. For the proposed temporal embedding propagation module, we design it to address the scale changes and deformation problem. In our method, the module can efficiently utilize the advantage of temporal information between the adjacent frames. The object information in the previous frame can be transformed into a single dimension embedding vector using our designed RoI embedding layer and propagate to the current frame. The operation can provide the current feature of the target object, and can also increase the discriminative power in the similarity stage. This study conducts several of experiments on the public benchmark OTB and the VOT challenges, and is compared against previous works. Though there are uncontrolled factors in the videos, such as illumination changes and fast motion, the proposed method can achieve superior accuracy than the former schemes. Thus, the proposed method has considerable potential to be applied in the practical applications.
Hsu, Yi-Kuan, and 許以觀. "A Factory-aware Attentional LSTM Model for PM2.5 Prediction." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/fcx28w.
Повний текст джерела國立交通大學
資訊管理研究所
107
With air quality issues becoming a global concern, many countries is facing lot of air pollution problems. While monitoring stations have been established to collect air quality information, and scientists have been committed to the study of air quality predictions, but few studies have taken the different monitoring areas and industrial features into account. In this paper, we propose a deep neural network for PM2.5 predictions, named FAA-LSTM, collecting air quality data from three types of monitors and factory data that is highly related to air quality. A spatial transformation component is designed to obtain the local factors by segmenting the monitoring areas into grids and we consider the influence of neighboring factory data over local PM2.5 grids by adopting attention mechanism to find out the importance. Next, the factor of global air quality station is considered. We combine these heterogeneous data and feed it into a long short-term memory neural network to extract the hidden features and forecast PM2.5 concentrations. In this research, we evaluate our model FAA-LSTM with data from EPA and Academia Sinica in Taichung, surpassing the results of multiple methods, including linear regression, support vector regression, multi-layer perceptron and LSTM.
Книги з теми "Attention aware"
Obuhova, Galina, and Galina Klimova. Fundamentals of public communication skills: practical recommendations. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1090527.
Повний текст джерелаUnited States. General Accounting Office. Accounting and Information Management Division. Congressional Award Foundation: Continuing attention needed to improve internal controls. Washington, D.C: The Office, 1998.
Знайти повний текст джерелаDivision, United States General Accounting Office Accounting and Information Management. Congressional Award Foundation: Continuing attention needed to improve internal controls. Washington, D.C: The Office, 1998.
Знайти повний текст джерелаKortazar, Jon. De la periferia al centro: nuevas escritoras vascas. Venice: Fondazione Università Ca’ Foscari, 2022. http://dx.doi.org/10.30687/978-88-6969-594-0.
Повний текст джерелаGaneri, Jonardon. Narrative Attention. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198757405.003.0012.
Повний текст джерелаGaneri, Jonardon. Attention, Not Self. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198757405.001.0001.
Повний текст джерелаCullen, John. Alert, Aware, Attentive: Advent Reflections. Messenger Publications, 2020.
Знайти повний текст джерелаGeo. R. & G. M. Tremaine (Firm), ed. [Letter]: We beg respectfully to call your attention to the annexed prospectus, you are probably aware of our having been engaged ... in surveying the various counties in the western province for the descriptive maps ... [Toronto?: s.n., 1986.
Знайти повний текст джерелаOsterhammel, Jürgen. World History. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199225996.003.0006.
Повний текст джерелаHackett, Rosalind I. J. Sound. Edited by Michael Stausberg and Steven Engler. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780198729570.013.22.
Повний текст джерелаЧастини книг з теми "Attention aware"
Liben, Stephen. "Class 1: Attention and Awareness." In MD Aware, 25–38. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22430-1_3.
Повний текст джерелаFan, Shaokun, and J. Leon Zhao. "Attention-Aware Collaboration Modeling." In Lecture Notes in Business Information Processing, 347–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29873-8_32.
Повний текст джерелаLi, Shanshan, Qiang Cai, Zhuangzi Li, Haisheng Li, Naiguang Zhang, and Jian Cao. "Attention-Aware Invertible Hashing Network." In Lecture Notes in Computer Science, 409–20. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34113-8_34.
Повний текст джерелаD’Mello, Sidney K. "Gaze-Based Attention-Aware Cyberlearning Technologies." In Mind, Brain and Technology, 87–105. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02631-8_6.
Повний текст джерелаMartin, Charles E., Dana Warmsley, and Samuel D. Johnson. "Optimizing Attention-Aware Opinion Seeding Strategies." In Social, Cultural, and Behavioral Modeling, 96–106. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61255-9_10.
Повний текст джерелаTu, Jingzheng, Guoxian Yu, Jun Wang, Carlotta Domeniconi, and Xiangliang Zhang. "Attention-Aware Answers of the Crowd." In Proceedings of the 2020 SIAM International Conference on Data Mining, 451–59. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2020. http://dx.doi.org/10.1137/1.9781611976236.51.
Повний текст джерелаLiu, Ziyi, Le Wang, and Nanning Zheng. "Content-Aware Attention Network for Action Recognition." In IFIP Advances in Information and Communication Technology, 109–20. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92007-8_10.
Повний текст джерелаWang, Peiyi, Hongtao Liu, Fangzhao Wu, Jinduo Song, Hongyan Xu, and Wenjun Wang. "REKA: Relation Extraction with Knowledge-Aware Attention." In Communications in Computer and Information Science, 62–73. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1956-7_6.
Повний текст джерелаPeng, Qiyao, Peiyi Wang, Wenjun Wang, Hongtao Liu, Yueheng Sun, and Pengfei Jiao. "NRSA: Neural Recommendation with Summary-Aware Attention." In Knowledge Science, Engineering and Management, 128–40. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29551-6_12.
Повний текст джерелаBai, Xinyi, Nan Yin, Xiang Zhang, Xin Wang, and Zhigang Luo. "Entity-Aware Biaffine Attention for Constituent Parsing." In Lecture Notes in Computer Science, 191–203. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86362-3_16.
Повний текст джерелаТези доповідей конференцій з теми "Attention aware"
Gupta, Ankit, and Jonathan Berant. "Value-aware Approximate Attention." In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.emnlp-main.753.
Повний текст джерелаLuo, Keyang, Tao Guan, Lili Ju, Yuesong Wang, Zhuo Chen, and Yawei Luo. "Attention-Aware Multi-View Stereo." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.00166.
Повний текст джерелаCordel, Macario O., Shaojing Fan, Zhiqi Shen, and Mohan S. Kankanhalli. "Emotion-Aware Human Attention Prediction." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.00415.
Повний текст джерелаYao, Yuan, Jianqiang Ren, Xuansong Xie, Weidong Liu, Yong-Jin Liu, and Jun Wang. "Attention-Aware Multi-Stroke Style Transfer." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.00156.
Повний текст джерелаZheng, Lei, Chun-Ta Lu, Lifang He, Sihong Xie, Huang He, Chaozhuo Li, Vahid Noroozi, Bowen Dong, and Philip S. Yu. "MARS: Memory Attention-Aware Recommender System." In 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2019. http://dx.doi.org/10.1109/dsaa.2019.00015.
Повний текст джерелаMaisonnasse, J., N. Gourier, O. Brdiczka, P. Reignier, and J. L. Crowley. "Detecting privacy in attention aware system." In 2nd IET International Conference on Intelligent Environments (IE 06). IEE, 2006. http://dx.doi.org/10.1049/cp:20060700.
Повний текст джерелаJin, Yuan, He Zhao, Ming Liu, Lan Du, and Wray Buntine. "Neural Attention-Aware Hierarchical Topic Model." In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.emnlp-main.80.
Повний текст джерелаChaturvedi, Saket S., Lan Zhang, and Xiaoyong Yuan. "Pay "Attention" to Adverse Weather: Weather-aware Attention-based Object Detection." In 2022 26th International Conference on Pattern Recognition (ICPR). IEEE, 2022. http://dx.doi.org/10.1109/icpr56361.2022.9956149.
Повний текст джерелаMcNamara, Ann, Katerina Mania, George Koulieris, and Laurent Itti. "Attention-aware rendering, mobile graphics and games." In ACM SIGGRAPH 2014 Courses. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2614028.2615416.
Повний текст джерелаHossain, Mohammad, Mehrdad Hosseinzadeh, Omit Chanda, and Yang Wang. "Crowd Counting Using Scale-Aware Attention Networks." In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2019. http://dx.doi.org/10.1109/wacv.2019.00141.
Повний текст джерелаЗвіти організацій з теми "Attention aware"
Jefferson, Brian. Reviewing Information Technology, Surveillance, and Race in the US. Just Tech, Social Science Research Council, May 2022. http://dx.doi.org/10.35650/jt.3033.d.2022.
Повний текст джерелаManaging Impulsive and Risky Behaviour – Episode 6 ‘ADHD, A Young Person’s Guide’. ACAMH, October 2022. http://dx.doi.org/10.13056/acamh.21276.
Повний текст джерела