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Artykuły w czasopismach na temat "Visual learning"
Sze, Daniel Y. "Visual Learning". Journal of Vascular and Interventional Radiology 32, nr 3 (marzec 2021): 331. http://dx.doi.org/10.1016/j.jvir.2021.01.265.
Pełny tekst źródłaLiu, Yan, Yang Liu, Shenghua Zhong i Songtao Wu. "Implicit Visual Learning". ACM Transactions on Intelligent Systems and Technology 8, nr 2 (18.01.2017): 1–24. http://dx.doi.org/10.1145/2974024.
Pełny tekst źródłaCruz, Rodrigo Santa, Basura Fernando, Anoop Cherian i Stephen Gould. "Visual Permutation Learning". IEEE Transactions on Pattern Analysis and Machine Intelligence 41, nr 12 (1.12.2019): 3100–3114. http://dx.doi.org/10.1109/tpami.2018.2873701.
Pełny tekst źródłaJones, Rachel. "Visual learning visualized". Nature Reviews Neuroscience 4, nr 1 (styczeń 2003): 10. http://dx.doi.org/10.1038/nrn1014.
Pełny tekst źródłaLu, Zhong-Lin, Tianmiao Hua, Chang-Bing Huang, Yifeng Zhou i Barbara Anne Dosher. "Visual perceptual learning". Neurobiology of Learning and Memory 95, nr 2 (luty 2011): 145–51. http://dx.doi.org/10.1016/j.nlm.2010.09.010.
Pełny tekst źródłaRichler, Jennifer J., i Thomas J. Palmeri. "Visual category learning". Wiley Interdisciplinary Reviews: Cognitive Science 5, nr 1 (26.11.2013): 75–94. http://dx.doi.org/10.1002/wcs.1268.
Pełny tekst źródłaNida, Diini Fitrahtun, Muhyiatul Fadilah, Ardi Ardi i Suci Fajrina. "CHARACTERISTICS OF VISUAL LITERACY-BASED BIOLOGY LEARNING MODULE VALIDITY ON PHOTOSYNTHESIS LEARNING MATERIALS". JURNAL PAJAR (Pendidikan dan Pengajaran) 7, nr 4 (29.07.2023): 785. http://dx.doi.org/10.33578/pjr.v7i4.9575.
Pełny tekst źródłaGuinibert, Matthew. "Learn from your environment: A visual literacy learning model". Australasian Journal of Educational Technology 36, nr 4 (28.09.2020): 173–88. http://dx.doi.org/10.14742/ajet.5200.
Pełny tekst źródłaTaga, Tadashi, Kazuhito Yoshizaki i Kimiko Kato. "Visual field difference in visual statistical learning." Proceedings of the Annual Convention of the Japanese Psychological Association 79 (22.09.2015): 2EV—074–2EV—074. http://dx.doi.org/10.4992/pacjpa.79.0_2ev-074.
Pełny tekst źródłaHolland, Keith. "Visual skills for learning". Set: Research Information for Teachers, nr 2 (1.08.1996): 1–4. http://dx.doi.org/10.18296/set.0900.
Pełny tekst źródłaRozprawy doktorskie na temat "Visual learning"
Zhu, Fan. "Visual feature learning". Thesis, University of Sheffield, 2015. http://etheses.whiterose.ac.uk/8218/.
Pełny tekst źródłaGoh, Hanlin. "Learning deep visual representations". Paris 6, 2013. http://www.theses.fr/2013PA066356.
Pełny tekst źródłaRecent 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.
Pełny tekst źródłaOwens, Andrew (Andrew Hale). "Learning visual models from paired audio-visual examples". Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/107352.
Pełny tekst źródłaCataloged 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.
Pełny tekst źródłaIn 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.
Pełny tekst źródłaTang-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.
Pełny tekst źródłaShi, Xiaojin. "Visual learning from small training datasets /". Diss., Digital Dissertations Database. Restricted to UC campuses, 2005. http://uclibs.org/PID/11984.
Pełny tekst źródłaLiu, 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.
Pełny tekst źródłaPh.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.
Pełny tekst źródłaKsiążki na temat "Visual learning"
Katsushi, Ikeuchi, i Veloso Manuela M, red. Symbolic visual learning. New York: Oxford University Press, 1997.
Znajdź pełny tekst źródłaK, Nayar Shree, i Poggio Tomaso, red. Early visual learning. New York: Oxford University Press, 1996.
Znajdź pełny tekst źródłaM, Moore David, i Dwyer Francis M, red. Visual literacy: A spectrum of visual learning. Englewood Cliffs, N.J: Educational Technology Publications, 1994.
Znajdź pełny tekst źródłaLiberty, Jesse. Learning Visual Basic .NET. Sebastopol, CA: O'Reilly, 2002.
Znajdź pełny tekst źródłaN, Erin Jane, red. Visual handicaps and learning. Wyd. 3. Austin, Tex: PRO-ED, 1992.
Znajdź pełny tekst źródłaVisual impact, visual teaching: Using images to strengthen learning. San Diego, Calif: Brain Store, Inc., 2005.
Znajdź pełny tekst źródłaRourke, Adrianne. Improving visual teaching materials. Hauppauge, N.Y: Nova Science Publishers, 2009.
Znajdź pełny tekst źródłaBaratta, Alex. Visual writing. Newcastle upon Tyne: Cambridge Scholars, 2010.
Znajdź pełny tekst źródłaVakanski, Aleksandar, i Farrokh Janabi-Sharifi. Robot Learning by Visual Observation. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119091882.
Pełny tekst źródłaBeatty, Grace Joely. PowerPoint: The visual learning guide. Rocklin, CA: Prima Pub., 1994.
Znajdź pełny tekst źródłaCzęści książek na temat "Visual learning"
Burge, M., i W. Burger. "Learning visual ideals". W Image Analysis and Processing, 316–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63508-4_138.
Pełny tekst źródłaBurge, M., i W. Burger. "Learning visual ideals". W Lecture Notes in Computer Science, 464–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0025067.
Pełny tekst źródłaPanciroli, Chiara, Laura Corazza i Anita Macauda. "Visual-Graphic Learning". W 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.
Pełny tekst źródłaLu, Zhong-Lin, i Barbara Anne Dosher. "Visual Perceptual Learning". W 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.
Pełny tekst źródłaLovegrove, William. "The Visual Deficit Hypothesis". W Learning Disabilities, 246–69. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4613-9133-3_8.
Pełny tekst źródłaGolon, Alexandra Shires. "Learning Styles Differentiation". W VISUAL-SPATIAL learners, 1–18. Wyd. 2. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003239482-1.
Pełny tekst źródłaGolon, Alexandra Shires. "Learning Styles Differentiation". W VISUAL-SPATIAL learners, 1–18. Wyd. 2. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003239482-1.
Pełny tekst źródłaWu, Qi, Peng Wang, Xin Wang, Xiaodong He i Wenwu Zhu. "Video Representation Learning". W Visual Question Answering, 111–17. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0964-1_7.
Pełny tekst źródłaWu, Qi, Peng Wang, Xin Wang, Xiaodong He i Wenwu Zhu. "Deep Learning Basics". W Visual Question Answering, 15–26. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0964-1_2.
Pełny tekst źródłaGrobstein, Paul, i Kao Liang Chow. "Visual System Development, Plasticity". W Learning and Memory, 56–58. Boston, MA: Birkhäuser Boston, 1989. http://dx.doi.org/10.1007/978-1-4899-6778-7_22.
Pełny tekst źródłaStreszczenia konferencji na temat "Visual learning"
Buijs, Jean M., i Michael S. Lew. "Learning visual concepts". W the seventh ACM international conference. New York, New York, USA: ACM Press, 1999. http://dx.doi.org/10.1145/319878.319880.
Pełny tekst źródłaZhao, Qi, i Christof Koch. "Learning visual saliency". W 2011 45th Annual Conference on Information Sciences and Systems (CISS). IEEE, 2011. http://dx.doi.org/10.1109/ciss.2011.5766178.
Pełny tekst źródłaBERARDI, NICOLETTA, i ADRIANA FIORENTINI. "VISUAL PERCEPTUAL LEARNING". W Proceedings of the International School of Biophysics. WORLD SCIENTIFIC, 2001. http://dx.doi.org/10.1142/9789812799975_0034.
Pełny tekst źródłaJi, Daomin, Hui Luo i Zhifeng Bao. "Visualization Recommendation Through Visual Relation Learning and Visual Preference Learning". W 2023 IEEE 39th International Conference on Data Engineering (ICDE). IEEE, 2023. http://dx.doi.org/10.1109/icde55515.2023.00145.
Pełny tekst źródłaGuangming Chang, Chunfen Yuan i Weiming Hu. "Interclass visual similarity based visual vocabulary learning". W 2011 First Asian Conference on Pattern Recognition (ACPR 2011). IEEE, 2011. http://dx.doi.org/10.1109/acpr.2011.6166597.
Pełny tekst źródłaMahouachi, Dorra, i Moulay A. Akhloufi. "Deep learning visual programming". W Disruptive Technologies in Information Sciences III, redaktorzy Misty Blowers, Russell D. Hall i Venkateswara R. Dasari. SPIE, 2019. http://dx.doi.org/10.1117/12.2519882.
Pełny tekst źródłaCruz, Rodrigo Santa, Basura Fernando, Anoop Cherian i Stephen Gould. "DeepPermNet: Visual Permutation Learning". W 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017. http://dx.doi.org/10.1109/cvpr.2017.640.
Pełny tekst źródłaCai, Haipeng, Shiv Raj Pant i Wen Li. "Towards learning visual semantics". W 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.
Pełny tekst źródłaTeow, Matthew Y. W. "Convolutional Visual Feature Learning". W the 2018 International Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3232651.3232672.
Pełny tekst źródłaYeh, Tom, i Trevor Darrell. "Dynamic visual category learning". W 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2008. http://dx.doi.org/10.1109/cvpr.2008.4587616.
Pełny tekst źródłaRaporty organizacyjne na temat "Visual learning"
Bhanu, Bir. Learning Integrated Visual Database for Image Exploitation. Fort Belvoir, VA: Defense Technical Information Center, listopad 2002. http://dx.doi.org/10.21236/ada413389.
Pełny tekst źródłaEdelman, Shimon, Heinrich H. Buelthoff i Erik Sklar. Task and Object Learning in Visual Recognition. Fort Belvoir, VA: Defense Technical Information Center, styczeń 1991. http://dx.doi.org/10.21236/ada259961.
Pełny tekst źródłaJiang, Yuhong V. Implicit Learning of Complex Visual Contexts Under Non-Optimal Conditions. Fort Belvoir, VA: Defense Technical Information Center, lipiec 2007. http://dx.doi.org/10.21236/ada482119.
Pełny tekst źródłaPetrie, Christopher, i Katija Aladin. Spotlight: Visual Arts. HundrED, grudzień 2020. http://dx.doi.org/10.58261/azgu5536.
Pełny tekst źródłaPoggio, Tomaso, i Stephen Smale. Hierarchical Kernel Machines: The Mathematics of Learning Inspired by Visual Cortex. Fort Belvoir, VA: Defense Technical Information Center, luty 2013. http://dx.doi.org/10.21236/ada580529.
Pełny tekst źródłaHarmon, Dr Jennifer. Exploring the Efficacy of Active and Authentic Learning in the Visual Merchandising Classroom. Ames: Iowa State University, Digital Repository, listopad 2016. http://dx.doi.org/10.31274/itaa_proceedings-180814-1524.
Pełny tekst źródłaMills, Kathy, Elizabeth Heck, Alinta Brown, Patricia Funnell i 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.
Pełny tekst źródłaYu, Wanchi. Implicit Learning of Children with and without Developmental Language Disorder across Auditory and Visual Categories. Portland State University Library, styczeń 2000. http://dx.doi.org/10.15760/etd.7460.
Pełny tekst źródłaNahorniak, 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, luty 2022. http://dx.doi.org/10.30970/vjo.2022.51.11412.
Pełny tekst źródłaShepiliev, Dmytro S., Yevhenii O. Modlo, Yuliia V. Yechkalo, Viktoriia V. Tkachuk, Mykhailo M. Mintii, Iryna S. Mintii, Oksana M. Markova i in. WebAR development tools: An overview. CEUR Workshop Proceedings, marzec 2021. http://dx.doi.org/10.31812/123456789/4356.
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