Добірка наукової літератури з теми "Visual learning"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Visual learning".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "Visual learning"
Sze, Daniel Y. "Visual Learning." Journal of Vascular and Interventional Radiology 32, no. 3 (March 2021): 331. http://dx.doi.org/10.1016/j.jvir.2021.01.265.
Повний текст джерелаLiu, Yan, Yang Liu, Shenghua Zhong, and Songtao Wu. "Implicit Visual Learning." ACM Transactions on Intelligent Systems and Technology 8, no. 2 (January 18, 2017): 1–24. http://dx.doi.org/10.1145/2974024.
Повний текст джерелаCruz, Rodrigo Santa, Basura Fernando, Anoop Cherian, and Stephen Gould. "Visual Permutation Learning." IEEE Transactions on Pattern Analysis and Machine Intelligence 41, no. 12 (December 1, 2019): 3100–3114. http://dx.doi.org/10.1109/tpami.2018.2873701.
Повний текст джерелаJones, Rachel. "Visual learning visualized." Nature Reviews Neuroscience 4, no. 1 (January 2003): 10. http://dx.doi.org/10.1038/nrn1014.
Повний текст джерелаLu, Zhong-Lin, Tianmiao Hua, Chang-Bing Huang, Yifeng Zhou, and Barbara Anne Dosher. "Visual perceptual learning." Neurobiology of Learning and Memory 95, no. 2 (February 2011): 145–51. http://dx.doi.org/10.1016/j.nlm.2010.09.010.
Повний текст джерелаRichler, Jennifer J., and Thomas J. Palmeri. "Visual category learning." Wiley Interdisciplinary Reviews: Cognitive Science 5, no. 1 (November 26, 2013): 75–94. http://dx.doi.org/10.1002/wcs.1268.
Повний текст джерелаNida, Diini Fitrahtun, Muhyiatul Fadilah, Ardi Ardi, and Suci Fajrina. "CHARACTERISTICS OF VISUAL LITERACY-BASED BIOLOGY LEARNING MODULE VALIDITY ON PHOTOSYNTHESIS LEARNING MATERIALS." JURNAL PAJAR (Pendidikan dan Pengajaran) 7, no. 4 (July 29, 2023): 785. http://dx.doi.org/10.33578/pjr.v7i4.9575.
Повний текст джерелаGuinibert, Matthew. "Learn from your environment: A visual literacy learning model." Australasian Journal of Educational Technology 36, no. 4 (September 28, 2020): 173–88. http://dx.doi.org/10.14742/ajet.5200.
Повний текст джерелаTaga, Tadashi, Kazuhito Yoshizaki, and Kimiko Kato. "Visual field difference in visual statistical learning." Proceedings of the Annual Convention of the Japanese Psychological Association 79 (September 22, 2015): 2EV—074–2EV—074. http://dx.doi.org/10.4992/pacjpa.79.0_2ev-074.
Повний текст джерелаHolland, Keith. "Visual skills for learning." Set: Research Information for Teachers, no. 2 (August 1, 1996): 1–4. http://dx.doi.org/10.18296/set.0900.
Повний текст джерелаДисертації з теми "Visual learning"
Zhu, Fan. "Visual feature learning." Thesis, University of Sheffield, 2015. http://etheses.whiterose.ac.uk/8218/.
Повний текст джерелаGoh, Hanlin. "Learning deep visual representations." Paris 6, 2013. http://www.theses.fr/2013PA066356.
Повний текст джерелаRecent advancements in the areas of deep learning and visual information processing have presented an opportunity to unite both fields. These complementary fields combine to tackle the problem of classifying images into their semantic categories. Deep learning brings learning and representational capabilities to a visual processing model that is adapted for image classification. This thesis addresses problems that lead to the proposal of learning deep visual representations for image classification. The problem of deep learning is tackled on two fronts. The first aspect is the problem of unsupervised learning of latent representations from input data. The main focus is the integration of prior knowledge into the learning of restricted Boltzmann machines (RBM) through regularization. Regularizers are proposed to induce sparsity, selectivity and topographic organization in the coding to improve discrimination and invariance. The second direction introduces the notion of gradually transiting from unsupervised layer-wise learning to supervised deep learning. This is done through the integration of bottom-up information with top-down signals. Two novel implementations supporting this notion are explored. The first method uses top-down regularization to train a deep network of RBMs. The second method combines predictive and reconstructive loss functions to optimize a stack of encoder-decoder networks. The proposed deep learning techniques are applied to tackle the image classification problem. The bag-of-words model is adopted due to its strengths in image modeling through the use of local image descriptors and spatial pooling schemes. Deep learning with spatial aggregation is used to learn a hierarchical visual dictionary for encoding the image descriptors into mid-level representations. This method achieves leading image classification performances for object and scene images. The learned dictionaries are diverse and non-redundant. The speed of inference is also high. From this, a further optimization is performed for the subsequent pooling step. This is done by introducing a differentiable pooling parameterization and applying the error backpropagation algorithm. This thesis represents one of the first attempts to synthesize deep learning and the bag-of-words model. This union results in many challenging research problems, leaving much room for further study in this area
Walker, Catherine Livesay. "Visual learning through Hypermedia." CSUSB ScholarWorks, 1996. https://scholarworks.lib.csusb.edu/etd-project/1148.
Повний текст джерелаOwens, Andrew (Andrew Hale). "Learning visual models from paired audio-visual examples." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/107352.
Повний текст джерелаCataloged from PDF version of thesis.
Includes bibliographical references (pages 93-104).
From the clink of a mug placed onto a saucer to the bustle of a busy café, our days are filled with visual experiences that are accompanied by distinctive sounds. In this thesis, we show that these sounds can provide a rich training signal for learning visual models. First, we propose the task of predicting the sound that an object makes when struck as a way of studying physical interactions within a visual scene. We demonstrate this idea by training an algorithm to produce plausible soundtracks for videos in which people hit and scratch objects with a drumstick. Then, with human studies and automated evaluations on recognition tasks, we verify that the sounds produced by the algorithm convey information about actions and material properties. Second, we show that ambient audio - e.g., crashing waves, people speaking in a crowd - can also be used to learn visual models. We train a convolutional neural network to predict a statistical summary of the sounds that occur within a scene, and we demonstrate that the visual representation learned by the model conveys information about objects and scenes.
by Andrew Owens.
Ph. D.
Peyre, Julia. "Learning to detect visual relations." Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLEE016.
Повний текст джерелаIn this thesis, we study the problem of detection of visual relations of the form (subject, predicate, object) in images, which are intermediate level semantic units between objects and complex scenes. Our work addresses two main challenges in visual relation detection: (1) the difficulty of obtaining box-level annotations to train fully-supervised models, (2) the variability of appearance of visual relations. We first propose a weakly-supervised approach which, given pre-trained object detectors, enables us to learn relation detectors using image-level labels only, maintaining a performance close to fully-supervised models. Second, we propose a model that combines different granularities of embeddings (for subject, object, predicate and triplet) to better model appearance variation and introduce an analogical reasoning module to generalize to unseen triplets. Experimental results demonstrate the improvement of our hybrid model over a purely compositional model and validate the benefits of our transfer by analogy to retrieve unseen triplets
Wang, Zhaoqing. "Self-supervised Visual Representation Learning." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/29595.
Повний текст джерелаTang-Wright, Kimmy. "Visual topography and perceptual learning in the primate visual system." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:388b9658-dceb-443a-a19b-c960af162819.
Повний текст джерелаShi, Xiaojin. "Visual learning from small training datasets /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2005. http://uclibs.org/PID/11984.
Повний текст джерелаLiu, Jingen. "Learning Semantic Features for Visual Recognition." Doctoral diss., University of Central Florida, 2009. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3358.
Повний текст джерелаPh.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Computer Science PhD
Beale, Dan. "Autonomous visual learning for robotic systems." Thesis, University of Bath, 2012. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.558886.
Повний текст джерелаКниги з теми "Visual learning"
Katsushi, Ikeuchi, and Veloso Manuela M, eds. Symbolic visual learning. New York: Oxford University Press, 1997.
Знайти повний текст джерелаK, Nayar Shree, and Poggio Tomaso, eds. Early visual learning. New York: Oxford University Press, 1996.
Знайти повний текст джерелаM, Moore David, and Dwyer Francis M, eds. Visual literacy: A spectrum of visual learning. Englewood Cliffs, N.J: Educational Technology Publications, 1994.
Знайти повний текст джерелаN, Erin Jane, ed. Visual handicaps and learning. 3rd ed. Austin, Tex: PRO-ED, 1992.
Знайти повний текст джерелаLiberty, Jesse. Learning Visual Basic .NET. Sebastopol, CA: O'Reilly, 2002.
Знайти повний текст джерелаRourke, Adrianne. Improving visual teaching materials. Hauppauge, N.Y: Nova Science Publishers, 2009.
Знайти повний текст джерелаBaratta, Alex. Visual writing. Newcastle upon Tyne: Cambridge Scholars, 2010.
Знайти повний текст джерелаManfred, Fahle, and Poggio Tomaso, eds. Perceptual learning. Cambridge, Mass: MIT Press, 2002.
Знайти повний текст джерелаVakanski, Aleksandar, and Farrokh Janabi-Sharifi. Robot Learning by Visual Observation. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119091882.
Повний текст джерелаBeatty, Grace Joely. PowerPoint: The visual learning guide. Rocklin, CA: Prima Pub., 1994.
Знайти повний текст джерелаЧастини книг з теми "Visual learning"
Burge, M., and W. Burger. "Learning visual ideals." In Image Analysis and Processing, 316–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63508-4_138.
Повний текст джерелаBurge, M., and W. Burger. "Learning visual ideals." In Lecture Notes in Computer Science, 464–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0025067.
Повний текст джерелаPanciroli, Chiara, Laura Corazza, and Anita Macauda. "Visual-Graphic Learning." In Proceedings of the 2nd International and Interdisciplinary Conference on Image and Imagination, 49–62. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41018-6_6.
Повний текст джерелаLu, Zhong-Lin, and Barbara Anne Dosher. "Visual Perceptual Learning." In Encyclopedia of the Sciences of Learning, 3415–18. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_258.
Повний текст джерелаLovegrove, William. "The Visual Deficit Hypothesis." In Learning Disabilities, 246–69. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4613-9133-3_8.
Повний текст джерелаGolon, Alexandra Shires. "Learning Styles Differentiation." In VISUAL-SPATIAL learners, 1–18. 2nd ed. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003239482-1.
Повний текст джерелаGolon, Alexandra Shires. "Learning Styles Differentiation." In VISUAL-SPATIAL learners, 1–18. 2nd ed. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003239482-1.
Повний текст джерелаWu, Qi, Peng Wang, Xin Wang, Xiaodong He, and Wenwu Zhu. "Video Representation Learning." In Visual Question Answering, 111–17. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0964-1_7.
Повний текст джерелаWu, Qi, Peng Wang, Xin Wang, Xiaodong He, and Wenwu Zhu. "Deep Learning Basics." In Visual Question Answering, 15–26. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0964-1_2.
Повний текст джерелаGrobstein, Paul, and Kao Liang Chow. "Visual System Development, Plasticity." In Learning and Memory, 56–58. Boston, MA: Birkhäuser Boston, 1989. http://dx.doi.org/10.1007/978-1-4899-6778-7_22.
Повний текст джерелаТези доповідей конференцій з теми "Visual learning"
Buijs, Jean M., and Michael S. Lew. "Learning visual concepts." In the seventh ACM international conference. New York, New York, USA: ACM Press, 1999. http://dx.doi.org/10.1145/319878.319880.
Повний текст джерелаZhao, Qi, and Christof Koch. "Learning visual saliency." In 2011 45th Annual Conference on Information Sciences and Systems (CISS). IEEE, 2011. http://dx.doi.org/10.1109/ciss.2011.5766178.
Повний текст джерелаBERARDI, NICOLETTA, and ADRIANA FIORENTINI. "VISUAL PERCEPTUAL LEARNING." In Proceedings of the International School of Biophysics. WORLD SCIENTIFIC, 2001. http://dx.doi.org/10.1142/9789812799975_0034.
Повний текст джерелаJi, Daomin, Hui Luo, and Zhifeng Bao. "Visualization Recommendation Through Visual Relation Learning and Visual Preference Learning." In 2023 IEEE 39th International Conference on Data Engineering (ICDE). IEEE, 2023. http://dx.doi.org/10.1109/icde55515.2023.00145.
Повний текст джерелаGuangming Chang, Chunfen Yuan, and Weiming Hu. "Interclass visual similarity based visual vocabulary learning." In 2011 First Asian Conference on Pattern Recognition (ACPR 2011). IEEE, 2011. http://dx.doi.org/10.1109/acpr.2011.6166597.
Повний текст джерелаMahouachi, Dorra, and Moulay A. Akhloufi. "Deep learning visual programming." In Disruptive Technologies in Information Sciences III, edited by Misty Blowers, Russell D. Hall, and Venkateswara R. Dasari. SPIE, 2019. http://dx.doi.org/10.1117/12.2519882.
Повний текст джерелаCruz, Rodrigo Santa, Basura Fernando, Anoop Cherian, and Stephen Gould. "DeepPermNet: Visual Permutation Learning." In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017. http://dx.doi.org/10.1109/cvpr.2017.640.
Повний текст джерелаCai, Haipeng, Shiv Raj Pant, and Wen Li. "Towards learning visual semantics." In ESEC/FSE '20: 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3368089.3417040.
Повний текст джерелаTeow, Matthew Y. W. "Convolutional Visual Feature Learning." In the 2018 International Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3232651.3232672.
Повний текст джерелаYeh, Tom, and Trevor Darrell. "Dynamic visual category learning." In 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2008. http://dx.doi.org/10.1109/cvpr.2008.4587616.
Повний текст джерелаЗвіти організацій з теми "Visual learning"
Bhanu, Bir. Learning Integrated Visual Database for Image Exploitation. Fort Belvoir, VA: Defense Technical Information Center, November 2002. http://dx.doi.org/10.21236/ada413389.
Повний текст джерелаEdelman, Shimon, Heinrich H. Buelthoff, and Erik Sklar. Task and Object Learning in Visual Recognition. Fort Belvoir, VA: Defense Technical Information Center, January 1991. http://dx.doi.org/10.21236/ada259961.
Повний текст джерелаJiang, Yuhong V. Implicit Learning of Complex Visual Contexts Under Non-Optimal Conditions. Fort Belvoir, VA: Defense Technical Information Center, July 2007. http://dx.doi.org/10.21236/ada482119.
Повний текст джерелаPetrie, Christopher, and Katija Aladin. Spotlight: Visual Arts. HundrED, December 2020. http://dx.doi.org/10.58261/azgu5536.
Повний текст джерелаPoggio, Tomaso, and Stephen Smale. Hierarchical Kernel Machines: The Mathematics of Learning Inspired by Visual Cortex. Fort Belvoir, VA: Defense Technical Information Center, February 2013. http://dx.doi.org/10.21236/ada580529.
Повний текст джерелаHarmon, Dr Jennifer. Exploring the Efficacy of Active and Authentic Learning in the Visual Merchandising Classroom. Ames: Iowa State University, Digital Repository, November 2016. http://dx.doi.org/10.31274/itaa_proceedings-180814-1524.
Повний текст джерелаMills, Kathy, Elizabeth Heck, Alinta Brown, Patricia Funnell, and Lesley Friend. Senses together : Multimodal literacy learning in primary education : Final project report. Institute for Learning Sciences and Teacher Education, Australian Catholic University, 2023. http://dx.doi.org/10.24268/acu.8zy8y.
Повний текст джерелаYu, Wanchi. Implicit Learning of Children with and without Developmental Language Disorder across Auditory and Visual Categories. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.7460.
Повний текст джерелаNahorniak, Maya. Occupation of profession: Methodology of laboratory classes from practically-oriented courses under distance learning (on an example of discipline «Radioproduction»). Ivan Franko National University of Lviv, February 2022. http://dx.doi.org/10.30970/vjo.2022.51.11412.
Повний текст джерелаShepiliev, Dmytro S., Yevhenii O. Modlo, Yuliia V. Yechkalo, Viktoriia V. Tkachuk, Mykhailo M. Mintii, Iryna S. Mintii, Oksana M. Markova, et al. WebAR development tools: An overview. CEUR Workshop Proceedings, March 2021. http://dx.doi.org/10.31812/123456789/4356.
Повний текст джерела