Dissertations / Theses on the topic 'Visual and performative learning'
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Zhu, Fan. "Visual feature learning." Thesis, University of Sheffield, 2015. http://etheses.whiterose.ac.uk/8218/.
Full textHuang, Wang. "Visual Sensation and Performative Cultural Politics: Chinese Literary Text Messages and the Colors of Texts." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1275499580.
Full textGoh, Hanlin. "Learning deep visual representations." Paris 6, 2013. http://www.theses.fr/2013PA066356.
Full textRecent 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.
Full textOwens, Andrew (Andrew Hale). "Learning visual models from paired audio-visual examples." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/107352.
Full textCataloged 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.
Gontovnik, Monica. "Another Way of Being: The Performative Practices of Contemporary Female ColombianArtists." Ohio University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1420473106.
Full textPeyre, Julia. "Learning to detect visual relations." Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLEE016.
Full textIn 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
Tang-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.
Full textShi, Xiaojin. "Visual learning from small training datasets /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2005. http://uclibs.org/PID/11984.
Full textLiu, 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.
Full textPh.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.
Full textLakshmi, Ratan Aparna. "Learning visual concepts for image classification." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80092.
Full textIncludes bibliographical references (leaves 166-174).
by Aparna Lakshmi Ratan.
Ph.D.
Moghaddam, Baback 1963. "Probabilistic visual learning for object detection." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/10242.
Full textIncludes bibliographical references (leaves 78-82).
by Baback Moghaddam.
Ph.D.
Wilson, Andrew David. "Learning visual behavior for gesture analysis." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/62924.
Full textZhou, Bolei. "Interpretable representation learning for visual intelligence." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/117837.
Full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 131-140).
Recent progress of deep neural networks in computer vision and machine learning has enabled transformative applications across robotics, healthcare, and security. However, despite the superior performance of the deep neural networks, it remains challenging to understand their inner workings and explain their output predictions. This thesis investigates several novel approaches for opening up the "black box" of neural networks used in visual recognition tasks and understanding their inner working mechanism. I first show that objects and other meaningful concepts emerge as a consequence of recognizing scenes. A network dissection approach is further introduced to automatically identify the internal units as the emergent concept detectors and quantify their interpretability. Then I describe an approach that can efficiently explain the output prediction for any given image. It sheds light on the decision-making process of the networks and why the predictions succeed or fail. Finally, I show some ongoing efforts toward learning efficient and interpretable deep representations for video event understanding and some future directions.
by Bolei Zhou.
Ph. D.
Pillai, Sudeep. "Learning articulated motions from visual demonstration." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/89861.
Full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
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Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 94-98).
Robots operating autonomously in household environments must be capable of interacting with articulated objects on a daily basis. They should be able to infer each object's underlying kinematic linkages purely by observing its motion during manipulation. This work proposes a framework that enables robots to learn the articulation in objects from user-provided demonstrations, using RGB-D sensors. We introduce algorithms that combine concepts in sparse feature tracking, motion segmentation, object pose estimation, and articulation learning, to develop our proposed framework. Additionally, our methods can predict the motion of previously seen articulated objects in future encounters. We present experiments that demonstrate the ability of our method, given RGB-D data, to identify, analyze and predict the articulation of a number of everyday objects within a human-occupied environment.
by Sudeep Pillai.
S.M. in Computer Science and Engineering
Williams, Oliver Michael Christian. "Bayesian learning for efficient visual inference." Thesis, University of Cambridge, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.613979.
Full textNorth, Ben. "Learning dynamical models for visual tracking." Thesis, University of Oxford, 1998. http://ora.ox.ac.uk/objects/uuid:6ed12552-4c30-4d80-88ef-7245be2d8fb8.
Full textFlorence, Peter R. (Peter Raymond). "Dense visual learning for robot manipulation." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/128398.
Full textThesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2020
Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages 115-127).
We would like to have highly useful robots which can richly perceive their world, semantically distinguish its fine details, and physically interact with it sufficiently for useful robotic manipulation. This is hard to achieve with previous methods: prior work has not equipped robots with the scalable ability to understand the dense visual state of their varied environments. The limitations have both been in the state representations used, and how to acquire them without significant human labeling effort. In this thesis we present work that leverages self-supervision, particularly via a mix of geometrical computer vision, deep visual learning, and robotic systems, to scalably produce dense visual inferences of the world state. These methods either enable robots to teach themselves dense visual models without human supervision, or they act as a large multiplying factor on the value of information provided by humans. Specifically, we develop a pipeline for providing ground truth labels of visual data in cluttered and multi-object scenes, we introduce the novel application of dense visual object descriptors to robotic manipulation and provide a fully robot-supervised pipeline to acquire them, and we leverage this dense visual understanding to efficiently learn new manipulation skills through imitation. With real robot hardware we demonstrate contact-rich tasks manipulating household objects, including generalizing across a class of objects, manipulating deformable objects, and manipulating a textureless symmetrical object, all with closed-loop, real-time vision-based manipulation policies.
by Peter R. Florence.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
Dey, Priya. "Visual speech in technology-enhanced learning." Thesis, University of Sheffield, 2012. http://etheses.whiterose.ac.uk/3329/.
Full textDurand, Thibaut. "Weakly supervised learning for visual recognition." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066142/document.
Full textThis thesis studies the problem of classification of images, where the goal is to predict if a semantic category is present in the image, based on its visual content. To analyze complex scenes, it is important to learn localized representations. To limit the cost of annotation during training, we have focused on weakly supervised learning approaches. In this thesis, we propose several models that simultaneously classify and localize objects, using only global labels during training. The weak supervision significantly reduces the cost of full annotation, but it makes learning more challenging. The key issue is how to aggregate local scores - e.g. regions - into global score - e.g. image. The main contribution of this thesis is the design of new pooling functions for weakly supervised learning. In particular, we propose a “max + min” pooling function, which unifies many pooling functions. We describe how to use this pooling in the Latent Structured SVM framework as well as in convolutional networks. To solve the optimization problems, we present several solvers, some of which allow to optimize a ranking metric such as Average Precision. We experimentally show the interest of our models with respect to state-of-the-art methods, on ten standard image classification datasets, including the large-scale dataset ImageNet
Nguyen, Duc Minh Chau. "Affordance learning for visual-semantic perception." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2021. https://ro.ecu.edu.au/theses/2443.
Full textSafavi, Seyed Mehdi. "A performative view of knowledge exploitation and exploration : a case study of a higher education merger." Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/17957.
Full textDoyon, Julien. "Right temporal-lobe contribution to global visual processing and visual-cue learning." Thesis, McGill University, 1988. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=75696.
Full textGepperth, Alexander Rainer Tassilo. "Neural learning methods for visual object detection." [S.l.] : [s.n.], 2006. http://deposit.ddb.de/cgi-bin/dokserv?idn=981053998.
Full textQin, Lei. "Online machine learning methods for visual tracking." Thesis, Troyes, 2014. http://www.theses.fr/2014TROY0017/document.
Full textWe study the challenging problem of tracking an arbitrary object in video sequences with no prior knowledge other than a template annotated in the first frame. To tackle this problem, we build a robust tracking system consisting of the following components. First, for image region representation, we propose some improvements to the region covariance descriptor. Characteristics of a specific object are taken into consideration, before constructing the covariance descriptor. Second, for building the object appearance model, we propose to combine the merits of both generative models and discriminative models by organizing them in a detection cascade. Specifically, generative models are deployed in the early layers for eliminating most easy candidates whereas discriminative models are in the later layers for distinguishing the object from a few similar "distracters". The Partial Least Squares Discriminant Analysis (PLS-DA) is employed for building the discriminative object appearance models. Third, for updating the generative models, we propose a weakly-supervised model updating method, which is based on cluster analysis using the mean-shift gradient density estimation procedure. Fourth, a novel online PLS-DA learning algorithm is developed for incrementally updating the discriminative models. The final tracking system that integrates all these building blocks exhibits good robustness for most challenges in visual tracking. Comparing results conducted in challenging video sequences showed that the proposed tracking system performs favorably with respect to a number of state-of-the-art methods
Pralle, Mandi Jo. "Visual design in the online learning environment." [Ames, Iowa : Iowa State University], 2007.
Find full textHussain, Sibt Ul. "Machine Learning Methods for Visual Object Detection." Phd thesis, Université de Grenoble, 2011. http://tel.archives-ouvertes.fr/tel-00680048.
Full textCabral, Ricardo da Silveira. "Unifying Low-Rank Models for Visual Learning." Research Showcase @ CMU, 2015. http://repository.cmu.edu/dissertations/506.
Full textXu, Yang. "Cortical spatiotemporal plasticity in visual category learning." Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/272.
Full textRamachandran, Suchitra. "Visual Statistical Learning in Monkey Inferotemporal Cortex." Research Showcase @ CMU, 2014. http://repository.cmu.edu/dissertations/463.
Full textFrier, Helen Jane. "Compass orientation during visual learning by honeybees." Thesis, University of Sussex, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.321446.
Full textKodirov, Elyor. "Cross-class transfer learning for visual data." Thesis, Queen Mary, University of London, 2017. http://qmro.qmul.ac.uk/xmlui/handle/123456789/31852.
Full textCrowley, Elliott Joseph. "Visual recognition in art using machine learning." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:d917f38e-64cb-4b09-9ccf-b081fe68b187.
Full textKashyap, Karan. "Learning digits via joint audio-visual representations." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/113143.
Full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 59-60).
Our goal is to explore models for language learning in the manner that humans learn languages as children. Namely, children do not have intermediary text transcriptions in correlating visual and audio inputs from the environment; rather, they directly make connections between what they see and what they hear, sometimes even across languages! In this thesis, we present weakly-supervised models for learning representations of numerical digits between two modalities: speech and images. We experiment with architectures of convolutional neural networks taking in spoken utterances of numerical digits and images of handwritten digits as inputs. In nearly all cases we randomly initialize network weights (without pre-training) and evaluate the model's ability to return a matching image for a spoken input or to identify the number of overlapping digits between an utterance and an image. We also provide some visuals as evidence that our models are truly learning correspondences between the two modalities.
by Karan Kashyap.
M. Eng.
Gilja, Vikash. "Learning and applying model-based visual context." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/33139.
Full textIncludes bibliographical references (p. 53).
I believe that context's ability to reduce the ambiguity of an input signal makes it a vital constraint for understanding the real world. I specifically examine the role of context in vision and how a model-based approach can aid visual search and recognition. Through the implementation of a system capable of learning visual context models from an image database, I demonstrate the utility of the model-based approach. The system is capable of learning models for "water-horizon scenes" and "suburban street scenes" from a database of 745 images.
by Vikash Gilja.
M.Eng.
Woodley, Thomas Edward. "Visual tracking using offline and online learning." Thesis, University of Cambridge, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.608814.
Full textNaha, Shujon. "Zero-shot Learning for Visual Recognition Problems." IEEE, 2015. http://hdl.handle.net/1993/31806.
Full textOctober 2016
Rao, Anantha N. "Learning-based Visual Odometry - A Transformer Approach." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627658636420617.
Full textHorn, Robert R. "Visual attention and information in observational learning." Thesis, Liverpool John Moores University, 2003. http://researchonline.ljmu.ac.uk/5624/.
Full textWhite, Alan Daniel. "Visual-motor learning in minimally invasive surgery." Thesis, University of Leeds, 2016. http://etheses.whiterose.ac.uk/17321/.
Full textBen-Younes, Hedi. "Multi-modal representation learning towards visual reasoning." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS173.
Full textThe quantity of images that populate the Internet is dramatically increasing. It becomes of critical importance to develop the technology for a precise and automatic understanding of visual contents. As image recognition systems are becoming more and more relevant, researchers in artificial intelligence now seek for the next generation vision systems that can perform high-level scene understanding. In this thesis, we are interested in Visual Question Answering (VQA), which consists in building models that answer any natural language question about any image. Because of its nature and complexity, VQA is often considered as a proxy for visual reasoning. Classically, VQA architectures are designed as trainable systems that are provided with images, questions about them and their answers. To tackle this problem, typical approaches involve modern Deep Learning (DL) techniques. In the first part, we focus on developping multi-modal fusion strategies to model the interactions between image and question representations. More specifically, we explore bilinear fusion models and exploit concepts from tensor analysis to provide tractable and expressive factorizations of parameters. These fusion mechanisms are studied under the widely used visual attention framework: the answer to the question is provided by focusing only on the relevant image regions. In the last part, we move away from the attention mechanism and build a more advanced scene understanding architecture where we consider objects and their spatial and semantic relations. All models are thoroughly experimentally evaluated on standard datasets and the results are competitive with the literature
Hanwell, David. "Weakly supervised learning of visual semantic attributes." Thesis, University of Bristol, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.687063.
Full textHussain, Sabit ul. "Machine Learning Methods for Visual Object Detection." Thesis, Grenoble, 2011. http://www.theses.fr/2011GRENM070/document.
Full textThe goal of this thesis is to develop better practical methods for detecting common object classes in real world images. We present a family of object detectors that combine Histogram of Oriented Gradient (HOG), Local Binary Pattern (LBP) and Local Ternary Pattern (LTP) features with efficient Latent SVM classifiers and effective dimensionality reduction and sparsification schemes to give state-of-the-art performance on several important datasets including PASCAL VOC2006 and VOC2007, INRIA Person and ETHZ. The three main contributions are as follows. Firstly, we pioneer the use of Local Ternary Pattern features for object detection, showing that LTP gives better overall performance than HOG and LBP, because it captures both rich local texture and object shape information while being resistant to variations in lighting conditions. It thus works well both for classes that are recognized mainly by their structure and ones that are recognized mainly by their textures. We also show that HOG, LBP and LTP complement one another, so that an extended feature set that incorporates all three of them gives further improvements in performance. Secondly, in order to tackle the speed and memory usage problems associated with high-dimensional modern feature sets, we propose two effective dimensionality reduction techniques. The first, feature projection using Partial Least Squares, allows detectors to be trained more rapidly with negligible loss of accuracy and no loss of run time speed for linear detectors. The second, feature selection using SVM weight truncation, allows active feature sets to be reduced in size by almost an order of magnitude with little or no loss, and often a small gain, in detector accuracy. Despite its simplicity, this feature selection scheme outperforms all of the other sparsity enforcing methods that we have tested. Lastly, we describe work in progress on Local Quantized Patterns (LQP), a generalized form of local pattern features that uses lookup table based vector quantization to provide local pattern style pixel neighbourhood codings that have the speed of LBP/LTP and some of the flexibility and power of traditional visual word representations. Our experiments show that LQP outperforms all of the other feature sets tested including HOG, LBP and LTP
Pyon, Wonn Sang. "Encoding Temporal Order and Visual Statistical Learning." Thesis, The University of Arizona, 2015. http://hdl.handle.net/10150/579050.
Full textLiu, Li. "Learning discriminative feature representations for visual categorization." Thesis, University of Sheffield, 2015. http://etheses.whiterose.ac.uk/8239/.
Full textJiang, Ping, and R. Unbehauen. "Robot visual servoing with iterative learning control." IEEE, 2002. http://hdl.handle.net/10454/3495.
Full textThis paper presents an iterative learning scheme for vision guided robot trajectory tracking. At first, a stability criterion for designing iterative learning controller is proposed. It can be used for a system with initial resetting error. By using the criterion, one can convert the design problem into finding a positive definite discrete matrix kernel and a more general form of learning control can be obtained. Then, a three-dimensional (3-D) trajectory tracking system with a single static camera to realize robot movement imitation is presented based on this criterion.
Al-Abood, Saleh Ahmed. "Effects of visual demonstrations on motor skill acquisition : a visual perception perspective." Thesis, Manchester Metropolitan University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340691.
Full textPiñol, Naranjo Mónica. "Reinforcement learning of visual descriptors for object recognition." Doctoral thesis, Universitat Autònoma de Barcelona, 2014. http://hdl.handle.net/10803/283927.
Full textThe human visual system is able to recognize the object in an image even if the object is partially occluded, from various points of view, in different colors, or with independence of the distance to the object. To do this, the eye obtains an image and extracts features that are sent to the brain, and then, in the brain the object is recognized. In computer vision, the object recognition branch tries to learns from the human visual system behaviour to achieve its goal. Hence, an algorithm is used to identify representative features of the scene (detection), then another algorithm is used to describe these points (descriptor) and finally the extracted information is used for classifying the object in the scene. The selection of this set of algorithms is a very complicated task and thus, a very active research field. In this thesis we are focused on the selection/learning of the best descriptor for a given image. In the state of the art there are several descriptors but we do not know how to choose the best descriptor because depends on scenes that we will use (dataset) and the algorithm chosen to do the classification. We propose a framework based on reinforcement learning and bag of features to choose the best descriptor according to the given image. The system can analyse the behaviour of different learning algorithms and descriptor sets. Further- more the proposed framework for improving the classification/recognition ratio can be used with minor changes in other computer vision fields, such as video retrieval.
Salazar, Rodrigo F. "Tob-down signals and learning in visual cortices /." [Zürich], 2004. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=15718.
Full text