Bibliografias temáticas / Visual representation learning

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Literatura científica selecionada sobre o tema "Visual representation learning"

Autor: Grafiati

Publicado: 28 de setembro de 2024

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Artigos de revistas sobre o assunto "Visual representation learning"

Lee, Jungmin, e Wongyoung Lee. "Aspects of A Study on the Multi Presentational Metaphor Education Using Online Telestration". Korean Society of Culture and Convergence 44, n.º 9 (30 de setembro de 2022): 163–73. http://dx.doi.org/10.33645/cnc.2022.9.44.9.163.

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This study is an attempt to propose a multiple representational metaphor education model that combines linguistic representation and visual representation using online telestration. The advent of the media and online era has incorporated not only the understanding of linguistic representation l but also the understanding of visual representation into an important phase of cognitive behavior and requires the implementation of online learning. In such an era's needs, it can be said that teaching-learning makes metaphors be used as a tool for thinking and cognition in an online environment, learning leads learners to a new horizon of perception by combining linguistic representation and visual representation. The multiple representational metaphor education model using online telestration will have a two-way dynamic interaction in an online environment, and it will be possible to improve learning capabilities by expressing various representations. Multiple representational metaphor education using online telestration will allow us to consider new perspectives and various possibilities of expression to interpret the world by converging and rephrasing verbal and visual representations using media in an online environment.

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Yang, Chuanguang, Zhulin An, Linhang Cai e Yongjun Xu. "Mutual Contrastive Learning for Visual Representation Learning". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 3 (28 de junho de 2022): 3045–53. http://dx.doi.org/10.1609/aaai.v36i3.20211.

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We present a collaborative learning method called Mutual Contrastive Learning (MCL) for general visual representation learning. The core idea of MCL is to perform mutual interaction and transfer of contrastive distributions among a cohort of networks. A crucial component of MCL is Interactive Contrastive Learning (ICL). Compared with vanilla contrastive learning, ICL can aggregate cross-network embedding information and maximize the lower bound to the mutual information between two networks. This enables each network to learn extra contrastive knowledge from others, leading to better feature representations for visual recognition tasks. We emphasize that the resulting MCL is conceptually simple yet empirically powerful. It is a generic framework that can be applied to both supervised and self-supervised representation learning. Experimental results on image classification and transfer learning to object detection show that MCL can lead to consistent performance gains, demonstrating that MCL can guide the network to generate better feature representations. Code is available at https://github.com/winycg/MCL.

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Khaerun Nisa, Rachmawati, e Reza Muhamad Zaenal. "Analysis Of Students' Mathematical Representation Ability in View of Learning Styles". Indo-MathEdu Intellectuals Journal 4, n.º 2 (15 de agosto de 2023): 99–109. http://dx.doi.org/10.54373/imeij.v4i2.119.

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Students' mathematical representation ability from the point of view of learning styles, we can see that learning styles play an important role in how students absorb mathematical information. The visual learning style involves using pictures, diagrams and other visual representations to understand math concepts. The auditory learning style involves a preference for listening to oral explanations or participating in discussions. Meanwhile, the kinesthetic learning style involves physical movement to gain an understanding of mathematics. This study aims to determine: 1) the representational ability of students who have a visual learning style, 2) the representational ability of students who have a kinesthetic learning style, 3) the representational ability of students who have an auditory learning style. In this study using descriptive qualitative research methods to determine the ability of mathematical representation which has a learning style for each student in class VII-F SMP Negeri 2 Kuningan. Data were collected through learning style questionnaires, representation ability tests and interviews with students. The results of the learning style questionnaire for the majority of students have a rich visual learning of 42%, kinesthetic 30% and auditory 28%. The results of the representation ability test showed that students with visual learning styles had high mathematical representation abilities, while the majority of students with auditory and kinesthetic learning styles were categorized as moderate. Interviews with students revealed that learning understanding can be understood by means of their learning styles. However, teachers face obstacles in choosing learning models that suit students' learning styles and need to understand individual learning needs. Students' activities in learning mathematics also still need to be improved, especially in the ability to discuss, conclude, and make summaries. Therefore, further efforts are needed to pay attention to differences in student abilities and develop appropriate strategies to increase student interaction and participation in learning mathematics.

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Kholilatun, Fiki, Nizaruddin Nizaruddin e F. X. Didik Purwosetiyono. "Kemampuan Representasi Siswa SMP Kelas VIII dalam Menyelesaikan Soal Cerita Materi Peluang Ditinjau dari Gaya Belajar Visual". Jurnal Kualita Pendidikan 4, n.º 1 (30 de abril de 2023): 54–59. http://dx.doi.org/10.51651/jkp.v4i1.339.

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This study aims to determine the representational abilities of class VIII junior high school students in solving word problems on opportunity material in terms of student learning styles. This research is a qualitative descriptive study. This research was conducted in class VIII B and VIII E of SMP Miftahul Ulum Boarding School Jogoloyo Demak which were selected based on the results of a questionnaire test. The subjects of this study were selected based on a learning style questionnaire of 60 grade VIII students, 6 students were selected consisting of 2 subjects with a visual learning style, 2 subjects with an auditory learning style, and 2 subjects with a kinesthetic learning style. . The instruments used in this study were a learning style questionnaire test to determine research subjects, math word problems to bring out students' representation abilities, and interview guidelines. The validity of the data uses technical triangulation, namely checking data that has been obtained from the same source using different techniques. The results of the research based on tests and interviews obtained 1) subjects with a visual learning style gave rise to visual representation indicators, and representations of mathematical equations or expressions. 2) subjects with learning styles bring up indicators of visual representation, and representation of equations or mathematical expressions. 3) subjects with kinesthetic learning styles only bring up visual representation indicators.

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Rif'at, Mohamad, Sudiansyah Sudiansyah e Khoirunnisa Imama. "Role of visual abilities in mathematics learning: An analysis of conceptual representation". Al-Jabar : Jurnal Pendidikan Matematika 15, n.º 1 (10 de junho de 2024): 87. http://dx.doi.org/10.24042/ajpm.v15i1.22406.

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Background: In mathematics education, the understanding of concepts is often influenced by students' visual abilities, making conceptual representation crucial in facilitating comprehension of the material.Aim: This study aims to analyze the role of visual abilities in facilitating the understanding of mathematical concepts through conceptual representation.Method: This research combines quantitative analysis with the use of attribute control diagrams to evaluate data obtained from tasks designed to test students' visual abilities in a mathematical context. These tasks include the manipulation of visual representations and problem-solving using geometric concepts.Results: The findings indicate that 80% of the sample possessed visual abilities that did not meet the expected index, showing a wide variation in students' visual representation abilities. Additionally, most students (70%) were more likely to choose familiar geometric representations in problem-solving, despite difficulties in manipulating more complex concepts.Conclusion: This study demonstrates that students often struggle to effectively utilize visual representations, preferring algebraic approaches that do not fully exploit the potential of conceptual representation. The findings suggest that an increased focus on developing visual abilities, especially in conceptual representation, could strengthen mathematical understanding. Further research is needed to develop intervention strategies that can help students overcome gaps in their visual abilities.

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Ruliani, Iva Desi, Nizaruddin Nizaruddin e Yanuar Hery Murtianto. "Profile Analysis of Mathematical Problem Solving Abilities with Krulik & Rudnick Stages Judging from Medium Visual Representation". JIPM (Jurnal Ilmiah Pendidikan Matematika) 7, n.º 1 (7 de setembro de 2018): 22. http://dx.doi.org/10.25273/jipm.v7i1.2123.

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The ability to solve mathematical problems is very important in learning math and everyday life. According to Krulik & Rudnick there are 5 stages of problem solving that is Read, Explore, Select A Strategy, Solve And Look Back. Mathematical problems require multiple representational skills to communicate problems, one of which is visual representation. Trigonometry is one of the materials that uses visual representation. This research is a qualitative descriptive research that aims to describe the ability of problem solving mathematics with Krulik & Rudnick stages in terms of visual representation. The study was conducted in MAN 2 Brebes. Determination of Subjects in this study using Purposive Sampling. Research instruments used to obtain the required data are visual representation and problem-solving tests, and interview guidelines. The data obtained were analyzed based on the Krulik & Rudnick problem solving indicator. Subjects in this study were subjects with moderate visual representation. Based on the results, problem solving ability of the subject is not fully fulfilled. Subjects with visual representations are able to do problem solving well that is solving the problem through a concept that is understood without visualization of the image. Subjects with visual representations are having a schematic visual representation type.

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Zha, B., e A. Yilmaz. "LEARNING MAPS FOR OBJECT LOCALIZATION USING VISUAL-INERTIAL ODOMETRY". ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-1-2020 (3 de agosto de 2020): 343–50. http://dx.doi.org/10.5194/isprs-annals-v-1-2020-343-2020.

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Abstract. Objects follow designated path on maps, such as vehicles travelling on a road. This observation signifies topological representation of objects’ motion on the map. Considering the position of object is unknown initially, as it traverses the map by moving and turning, the spatial uncertainty of its whereabouts reduces to a single location as the motion trajectory would fit only to a certain map trajectory. Inspired by this observation, we propose a novel end-to-end localization approach based on topological maps that exploits the object motion and learning the map using an recurrent neural network (RNN) model. The core of the proposed method is to learn potential motion patterns from the map and perform trajectory classification in the map’s edge-space. Two different trajectory representations, namely angle representation and augmented angle representation (incorporates distance traversed) are considered and an RNN is trained from the map for each representation to compare their performances. The localization accuracy in the tested map for the angle and augmented angle representations are 90.43% and 96.22% respectively. The results from the actual visual-inertial odometry have shown that the proposed approach is able to learn the map and localize objects based on their motion.

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Moghaddam, B., e A. Pentland. "Probabilistic visual learning for object representation". IEEE Transactions on Pattern Analysis and Machine Intelligence 19, n.º 7 (julho de 1997): 696–710. http://dx.doi.org/10.1109/34.598227.

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He, Xiangteng, e Yuxin Peng. "Fine-Grained Visual-Textual Representation Learning". IEEE Transactions on Circuits and Systems for Video Technology 30, n.º 2 (fevereiro de 2020): 520–31. http://dx.doi.org/10.1109/tcsvt.2019.2892802.

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Liu, Qiyuan, Qi Zhou, Rui Yang e Jie Wang. "Robust Representation Learning by Clustering with Bisimulation Metrics for Visual Reinforcement Learning with Distractions". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 7 (26 de junho de 2023): 8843–51. http://dx.doi.org/10.1609/aaai.v37i7.26063.

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Recent work has shown that representation learning plays a critical role in sample-efficient reinforcement learning (RL) from pixels. Unfortunately, in real-world scenarios, representation learning is usually fragile to task-irrelevant distractions such as variations in background or viewpoint. To tackle this problem, we propose a novel clustering-based approach, namely Clustering with Bisimulation Metrics (CBM), which learns robust representations by grouping visual observations in the latent space. Specifically, CBM alternates between two steps: (1) grouping observations by measuring their bisimulation distances to the learned prototypes; (2) learning a set of prototypes according to the current cluster assignments. Computing cluster assignments with bisimulation metrics enables CBM to capture task-relevant information, as bisimulation metrics quantify the behavioral similarity between observations. Moreover, CBM encourages the consistency of representations within each group, which facilitates filtering out task-irrelevant information and thus induces robust representations against distractions. An appealing feature is that CBM can achieve sample-efficient representation learning even if multiple distractions exist simultaneously. Experiments demonstrate that CBM significantly improves the sample efficiency of popular visual RL algorithms and achieves state-of-the-art performance on both multiple and single distraction settings. The code is available at https://github.com/MIRALab-USTC/RL-CBM.

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Mais fontes

Teses / dissertações sobre o assunto "Visual representation learning"

Wang, Zhaoqing. "Self-supervised Visual Representation Learning". Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/29595.

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In general, large-scale annotated data are essential to training deep neural networks in order to achieve better performance in visual feature learning for various computer vision applications. Unfortunately, the amount of annotations is challenging to obtain, requiring a high cost of money and human resources. The dependence on large-scale annotated data has become a crucial bottleneck in developing an advanced intelligence perception system. Self-supervised visual representation learning, a subset of unsupervised learning, has gained popularity because of its ability to avoid the high cost of annotated data. A series of methods designed various pretext tasks to explore the general representations from unlabeled data and use these general representations for different downstream tasks. Although previous methods achieved great success, the label noise problem exists in these pretext tasks due to the lack of human-annotation supervision, which causes harmful effects on the transfer performance. This thesis discusses two types of the noise problem in self-supervised learning and designs the corresponding methods to alleviate the negative effects and explore the transferable representations. Firstly, in pixel-level self-supervised learning, the pixel-level correspondences are easily noisy because of complicated context relationships (e.g., misleading pixels in the background). Secondly, two views of the same image share the foreground object and some background information. As optimizing the pretext task (e.g., contrastive learning), the model is easily to capture the foreground object and noisy background information, simultaneously. Such background information can be harmful to the transfer performance on downstream tasks, including image classification, object detection, and instance segmentation. To address the above mentioned issues, our core idea is to leverage the data regularities and prior knowledge. Experimental results demonstrate that the proposed methods effectively alleviate the negative effects of label noise in self-supervised learning and surpass a series of previous methods.

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Zhou, Bolei. "Interpretable representation learning for visual intelligence". Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/117837.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This 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.

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Ben-Younes, Hedi. "Multi-modal representation learning towards visual reasoning". Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS173.

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La quantité d'images présentes sur internet augmente considérablement, et il est nécessaire de développer des techniques permettant le traitement automatique de ces contenus. Alors que les méthodes de reconnaissance visuelle sont de plus en plus évoluées, la communauté scientifique s'intéresse désormais à des systèmes aux capacités de raisonnement plus poussées. Dans cette thèse, nous nous intéressons au Visual Question Answering (VQA), qui consiste en la conception de systèmes capables de répondre à une question portant sur une image. Classiquement, ces architectures sont conçues comme des systèmes d'apprentissage automatique auxquels on fournit des images, des questions et leur réponse. Ce problème difficile est habituellement abordé par des techniques d'apprentissage profond. Dans la première partie de cette thèse, nous développons des stratégies de fusion multimodales permettant de modéliser des interactions entre les représentations d'image et de question. Nous explorons des techniques de fusion bilinéaire, et assurons l'expressivité et la simplicité des modèles en utilisant des techniques de factorisation tensorielle. Dans la seconde partie, on s'intéresse au raisonnement visuel qui encapsule ces fusions. Après avoir présenté les schémas classiques d'attention visuelle, nous proposons une architecture plus avancée qui considère les objets ainsi que leurs relations mutuelles. Tous les modèles sont expérimentalement évalués sur des jeux de données standards et obtiennent des résultats compétitifs avec ceux de la littérature
The 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

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Sharif, Razavian Ali. "Convolutional Network Representation for Visual Recognition". Doctoral thesis, KTH, Robotik, perception och lärande, RPL, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-197919.

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Image representation is a key component in visual recognition systems. In visual recognition problem, the solution or the model should be able to learn and infer the quality of certain visual semantics in the image. Therefore, it is important for the model to represent the input image in a way that the semantics of interest can be inferred easily and reliably. This thesis is written in the form of a compilation of publications and tries to look into the Convolutional Networks (CovnNets) representation in visual recognition problems from an empirical perspective. Convolutional Network is a special class of Neural Networks with a hierarchical structure where every layer’s output (except for the last layer) will be the input of another one. It was shown that ConvNets are powerful tools to learn a generic representation of an image. In this body of work, we first showed that this is indeed the case and ConvNet representation with a simple classifier can outperform highly-tuned pipelines based on hand-crafted features. To be precise, we first trained a ConvNet on a large dataset, then for every image in another task with a small dataset, we feedforward the image to the ConvNet and take the ConvNets activation on a certain layer as the image representation. Transferring the knowledge from the large dataset (source task) to the small dataset (target task) proved to be effective and outperformed baselines on a variety of tasks in visual recognition. We also evaluated the presence of spatial visual semantics in ConvNet representation and observed that ConvNet retains significant spatial information despite the fact that it has never been explicitly trained to preserve low-level semantics. We then tried to investigate the factors that affect the transferability of these representations. We studied various factors on a diverse set of visual recognition tasks and found a consistent correlation between the effect of those factors and the similarity of the target task to the source task. This intuition alongside the experimental results provides a guideline to improve the performance of visual recognition tasks using ConvNet features. Finally, we addressed the task of visual instance retrieval specifically as an example of how these simple intuitions can increase the performance of the target task massively.

QC 20161209

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Yu, Mengyang. "Feature reduction and representation learning for visual applications". Thesis, Northumbria University, 2016. http://nrl.northumbria.ac.uk/30222/.

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Computation on large-scale data spaces has been involved in many active problems in computer vision and pattern recognition. However, in realistic applications, most existing algorithms are heavily restricted by the large number of features, and tend to be inefficient and even infeasible. In this thesis, the solution to this problem is addressed in the following ways: (1) projecting features onto a lower-dimensional subspace; (2) embedding features into a Hamming space. Firstly, a novel subspace learning algorithm called Local Feature Discriminant Projection (LFDP) is proposed for discriminant analysis of local features. LFDP is able to efficiently seek a subspace to improve the discriminability of local features for classification. Extensive experimental validation on three benchmark datasets demonstrates that the proposed LFDP outperforms other dimensionality reduction methods and achieves state-of-the-art performance for image classification. Secondly, for action recognition, a novel binary local representation for RGB-D video data fusion is presented. In this approach, a general local descriptor called Local Flux Feature (LFF) is obtained for both RGB and depth data by computing the local fluxes of the gradient fields of video data. Then the LFFs from RGB and depth channels are fused into a Hamming space via the Structure Preserving Projection (SPP), which preserves not only the pairwise feature structure, but also a higher level connection between samples and classes. Comprehensive experimental results show the superiority of both LFF and SPP. Thirdly, in respect of unsupervised learning, SPP is extended to the Binary Set Embedding (BSE) for cross-modal retrieval. BSE outputs meaningful hash codes for local features from the image domain and word vectors from text domain. Extensive evaluation on two widely-used image-text datasets demonstrates the superior performance of BSE compared with state-of-the-art cross-modal hashing methods. Finally, a generalized multiview spectral embedding algorithm called Kernelized Multiview Projection (KMP) is proposed to fuse the multimedia data from multiple sources. Different features/views in the reproducing kernel Hilbert spaces are linearly fused together and then projected onto a low-dimensional subspace by KMP, whose performance is thoroughly evaluated on both image and video datasets compared with other multiview embedding methods.

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Venkataramanan, Shashanka. "Metric learning for instance and category-level visual representation". Electronic Thesis or Diss., Université de Rennes (2023-....), 2024. http://www.theses.fr/2024URENS022.

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Le principal objectif de la vision par ordinateur est de permettre aux machines d'extraire des informations significatives à partir de données visuelles, telles que des images et des vidéos, et de tirer parti de ces informations pour effectuer une large gamme de tâches. À cette fin, de nombreuses recherches se sont concentrées sur le développement de modèles d'apprentissage profond capables de coder des représentations visuelles complètes et robustes. Une stratégie importante dans ce contexte consiste à préentraîner des modèles sur des ensembles de données à grande échelle, tels qu'ImageNet, pour apprendre des représentations qui peuvent présenter une applicabilité transversale aux tâches et faciliter la gestion réussie de diverses tâches en aval avec un minimum d'effort. Pour faciliter l'apprentissage sur ces ensembles de données à grande échelle et coder de bonnes représentations, des stratégies complexes d'augmentation des données ont été utilisées. Cependant, ces augmentations peuvent être limitées dans leur portée, étant soit conçues manuellement et manquant de diversité, soit générant des images qui paraissent artificielles. De plus, ces techniques d'augmentation se sont principalement concentrées sur le jeu de données ImageNet et ses tâches en aval, limitant leur applicabilité à un éventail plus large de problèmes de vision par ordinateur. Dans cette thèse, nous visons à surmonter ces limitations en explorant différentes approches pour améliorer l'efficacité et l'efficience de l'apprentissage des représentations. Le fil conducteur des travaux présentés est l'utilisation de techniques basées sur l'interpolation, telles que mixup, pour générer des exemples d'entraînement diversifiés et informatifs au-delà du jeu de données original. Dans le premier travail, nous sommes motivés par l'idée de la déformation comme un moyen naturel d'interpoler des images plutôt que d'utiliser une combinaison convexe. Nous montrons que l'alignement géométrique des deux images dans l'espace des caractéristiques permet une interpolation plus naturelle qui conserve la géométrie d'une image et la texture de l'autre, la reliant au transfert de style. En nous appuyant sur ces observations, nous explorons la combinaison de mix6up et de l'apprentissage métrique profond. Nous développons une formulation généralisée qui intègre mix6up dans l'apprentissage métrique, conduisant à des représentations améliorées qui explorent des zones de l'espace d'embedding au-delà des classes d'entraînement. En nous appuyant sur ces insights, nous revisitons la motivation originale de mixup et générons un plus grand nombre d'exemples interpolés au-delà de la taille du mini-lot en interpolant dans l'espace d'embedding. Cette approche nous permet d'échantillonner sur l'ensemble de l'enveloppe convexe du mini-lot, plutôt que juste le long des segments linéaires entre les paires d'exemples. Enfin, nous explorons le potentiel de l'utilisation d'augmentations naturelles d'objets à partir de vidéos. Nous introduisons un ensemble de données "Walking Tours" de vidéos égocentriques en première personne, qui capturent une large gamme d'objets et d'actions dans des transitions de scènes naturelles. Nous proposons ensuite une nouvelle méthode de préentraînement auto-supervisée appelée DoRA, qui détecte et suit des objets dans des images vidéo, dérivant de multiples vues à partir des suivis et les utilisant de manière auto-supervisée
The primary goal in computer vision is to enable machines to extract meaningful information from visual data, such as images and videos, and leverage this information to perform a wide range of tasks. To this end, substantial research has focused on developing deep learning models capable of encoding comprehensive and robust visual representations. A prominent strategy in this context involves pretraining models on large-scale datasets, such as ImageNet, to learn representations that can exhibit cross-task applicability and facilitate the successful handling of diverse downstream tasks with minimal effort. To facilitate learning on these large-scale datasets and encode good representations, com- plex data augmentation strategies have been used. However, these augmentations can be limited in their scope, either being hand-crafted and lacking diversity, or generating images that appear unnatural. Moreover, the focus of these augmentation techniques has primarily been on the ImageNet dataset and its downstream tasks, limiting their applicability to a broader range of computer vision problems. In this thesis, we aim to tackle these limitations by exploring different approaches to en- hance the efficiency and effectiveness in representation learning. The common thread across the works presented is the use of interpolation-based techniques, such as mixup, to generate diverse and informative training examples beyond the original dataset. In the first work, we are motivated by the idea of deformation as a natural way of interpolating images rather than using a convex combination. We show that geometrically aligning the two images in the fea- ture space, allows for more natural interpolation that retains the geometry of one image and the texture of the other, connecting it to style transfer. Drawing from these observations, we explore the combination of mixup and deep metric learning. We develop a generalized formu- lation that accommodates mixup in metric learning, leading to improved representations that explore areas of the embedding space beyond the training classes. Building on these insights, we revisit the original motivation of mixup and generate a larger number of interpolated examples beyond the mini-batch size by interpolating in the embedding space. This approach allows us to sample on the entire convex hull of the mini-batch, rather than just along lin- ear segments between pairs of examples. Finally, we investigate the potential of using natural augmentations of objects from videos. We introduce a "Walking Tours" dataset of first-person egocentric videos, which capture a diverse range of objects and actions in natural scene transi- tions. We then propose a novel self-supervised pretraining method called DoRA, which detects and tracks objects in video frames, deriving multiple views from the tracks and using them in a self-supervised manner

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Li, Nuo Ph D. Massachusetts Institute of Technology. "Unsupervised learning of invariant object representation in primate visual cortex". Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/65288.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references.
Visual object recognition (categorization and identification) is one of the most fundamental cognitive functions for our survival. Our visual system has the remarkable ability to convey to us visual object and category information in a manner that is largely tolerant ("invariant") to the exact position, size, pose of the object, illumination, and clutter. The ventral visual stream in non-human primate has solved this problem. At the highest stage of the visual hierarchy, the inferior temporal cortex (IT), neurons have selectivity for objects and maintain that selectivity across variations in the images. A reasonably sized population of these tolerant neurons can support object recognition. However, we do not yet understand how IT neurons construct this neuronal tolerance. The aim of this thesis is to tackle this question and to examine the hypothesis that the ventral visual stream may leverage experience to build its neuronal tolerance. One potentially powerful idea is that time can act as an implicit teacher, in that each object's identity tends to remain temporally stable, thus different retinal images of the same object are temporally contiguous. In theory, the ventral stream could take advantage of this natural tendency and learn to associate together the neuronal representations of temporally contiguous retinal images to yield tolerant object selectivity in IT cortex. In this thesis, I report neuronal support for this hypothesis in IT of non-human primates. First, targeted alteration of temporally contiguous experience with object images at different retinal positions rapidly reshaped IT neurons' position tolerance. Second, similar temporal contiguity manipulation of experience with object images at different sizes similarly reshaped IT size tolerance. These instances of experience-induced effect were similar in magnitude, grew gradually stronger with increasing visual experience, and the size of the effect was large. Taken together, these studies show that unsupervised, temporally contiguous experience can reshape and build at least two types of IT tolerance, and that they can do so under a wide range of spatiotemporal regimes encountered during natural visual exploration. These results suggest that the ventral visual stream uses temporal contiguity visual experience with a general unsupervised tolerance learning (UTL) mechanism to build its invariant object representation.
by Nuo Li.
Ph.D.

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Dalens, Théophile. "Learnable factored image representation for visual discovery". Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLEE036.

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L'objectif de cette thèse est de développer des outils pour analyser les collections d'images temporelles afin d'identifier et de mettre en évidence les tendances visuelles à travers le temps. Cette thèse propose une approche pour l'analyse de données visuelles non appariées annotées avec le temps en générant à quoi auraient ressemblé les images si elles avaient été d'époques différentes. Pour isoler et transférer les variations d'apparence dépendantes du temps, nous introduisons un nouveau module bilinéaire de séparation de facteurs qui peut être entraîné. Nous analysons sa relation avec les représentations factorisées classiques et les auto-encodeurs basés sur la concaténation. Nous montrons que ce nouveau module présente des avantages par rapport à un module standard de concaténation lorsqu'il est utilisé dans une architecture de réseau de neurones convolutionnel encodeur-décodeur à goulot. Nous montrons également qu'il peut être inséré dans une architecture récente de traduction d'images à adversaire, permettant la transformation d'images à différentes périodes de temps cibles en utilisant un seul réseau
This thesis proposes an approach for analyzing unpaired visual data annotated with time stamps by generating how images would have looked like if they were from different times. To isolate and transfer time dependent appearance variations, we introduce a new trainable bilinear factor separation module. We analyze its relation to classical factored representations and concatenation-based auto-encoders. We demonstrate this new module has clear advantages compared to standard concatenation when used in a bottleneck encoder-decoder convolutional neural network architecture. We also show that it can be inserted in a recent adversarial image translation architecture, enabling the image transformation to multiple different target time periods using a single network

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Jonaityte, Inga <1981&gt. "Visual representation and financial decision making". Doctoral thesis, Università Ca' Foscari Venezia, 2014. http://hdl.handle.net/10579/4593.

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This thesis addresses experimentally three topics concerning the effects of visual representations on financial decision making. First, we hypothesize that visual representation of financial information affects comprehension and decision-making processes and outcomes. To test our hypothesis, we conducted online experiments demonstrating that the choice of visual representation leads to shifts in attention, comprehension, and evaluation of the information. The second study focuses on the ability of financial advisers to provide expert judgment to aid naïve consumers facing financial decisions. We found that advertising content significantly affects both experts and novices. Our results provide a previously underexplored viewpoint of decision making by finance professionals. The third topic concerns our ability to learn from multiple cues, adapt to changes, and develop new strategies. We investigated the effects of salient cues and environmental changes on learning, and found, among other things, that “abrupt” transformations in an environment are more harmful than “smooth” ones.
Questa tesi affronta sperimentalmente gli effetti delle rappresentazioni visive sulle decisioni finanziarie. Ipotizziamo che le rappresentazioni visive dell'informazione finanziaria possano influenzare le decisioni. Per testare tali ipotesi, abbiamo condotto esperimenti online e mostrato che la scelta della rappresentazione visiva conduce a cambiamenti nell'attenzione, comprensione, e valutazione dell'informazione. Il secondo studio riguarda l'abilità dei consulenti finanziari di offrire giudizio esperto per aiutare consumatori inesperti nelle decisioni finanziarie. Abbiamo trovato che il contenuto della pubblicità influenza significativamente tanto l'esperto quanto l'inesperto, il che offre una nuova prospettiva sulle decisioni dei consulenti finanziari. Il terzo tema riguarda l'apprendimento da informazioni multidimensionali, l'adattamento al cambiamento e lo sviluppo di nuove strategie. Abbiamo investigato gli effetti dell'importanza delle "cues" e di cambiamenti dell'ambiente decisionale sull'apprendimento. Trasformazioni improvvise nell'ambiente decisionale sono più dannose di trasformazioni graduali.

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Büchler, Uta [Verfasser], e Björn [Akademischer Betreuer] Ommer. "Visual Representation Learning with Minimal Supervision / Uta Büchler ; Betreuer: Björn Ommer". Heidelberg : Universitätsbibliothek Heidelberg, 2021. http://d-nb.info/1225868505/34.

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Livros sobre o assunto "Visual representation learning"

Virk, Satyugjit Singh. Learning STEM Through Integrative Visual Representation. [New York, N.Y.?]: [publisher not identified], 2013.

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Zhang, Zheng. Binary Representation Learning on Visual Images. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2112-2.

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Cheng, Hong. Sparse Representation, Modeling and Learning in Visual Recognition. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6714-3.

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1966-, McBride Kecia Driver, ed. Visual media and the humanities: A pedagogy of representation. Knoxville: University of Tennessee Press, 2004.

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Zareian, Alireza. Learning Structured Representations for Understanding Visual and Multimedia Data. [New York, N.Y.?]: [publisher not identified], 2021.

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I, Rumiati Raffaella, e Caramazza Alfonso, eds. The Multiple functions of sensory-motor representations. Hove: Psychology Press, 2005.

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Spiliotopoulou-Papantoniou, Vasiliki. The changing role of visual representations as a tool for research and learning. Hauppauge, N.Y: Nova Science Publishers, 2011.

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Learning-Based Local Visual Representation and Indexing. Elsevier, 2015. http://dx.doi.org/10.1016/c2014-0-01997-1.

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Ji, Rongrong, Yue Gao, Ling-Yu Duan, Qionghai Dai e Yao Hongxun. Learning-Based Local Visual Representation and Indexing. Elsevier Science & Technology Books, 2015.

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Learning-Based Local Visual Representation and Indexing. Elsevier Science & Technology Books, 2015.

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Mais fontes

Capítulos de livros sobre o assunto "Visual representation learning"

Wu, Qi, Peng Wang, Xin Wang, Xiaodong He e 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.

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Zhang, Zheng. "Correction to: Binary Representation Learning on Visual Images: Learning to Hash for Similarity Search". In Binary Representation Learning on Visual Images, C1—C2. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2112-2_8.

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Zhang, Zheng. "Deep Collaborative Graph Hashing". In Binary Representation Learning on Visual Images, 143–67. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2112-2_6.

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Zhang, Zheng. "Scalable Supervised Asymmetric Hashing". In Binary Representation Learning on Visual Images, 17–50. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2112-2_2.

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Zhang, Zheng. "Probability Ordinal-Preserving Semantic Hashing". In Binary Representation Learning on Visual Images, 81–109. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2112-2_4.

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Zhang, Zheng. "Introduction". In Binary Representation Learning on Visual Images, 1–16. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2112-2_1.

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Zhang, Zheng. "Semantic-Aware Adversarial Training". In Binary Representation Learning on Visual Images, 169–97. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2112-2_7.

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Zhang, Zheng. "Inductive Structure Consistent Hashing". In Binary Representation Learning on Visual Images, 51–80. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2112-2_3.

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Zhang, Zheng. "Ordinal-Preserving Latent Graph Hashing". In Binary Representation Learning on Visual Images, 111–41. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2112-2_5.

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Guo, Tan, Lei Zhang e Xiaoheng Tan. "Extreme Latent Representation Learning for Visual Classification". In Proceedings in Adaptation, Learning and Optimization, 65–75. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23307-5_8.

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Trabalhos de conferências sobre o assunto "Visual representation learning"

Chen, Guikun, Xia Li, Yi Yang e Wenguan Wang. "Neural Clustering Based Visual Representation Learning". In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 5714–25. IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.00546.

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Brack, Viktor, e Dominik Koßmann. "Local Representation Learning Using Visual Priors for Remote Sensing". In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, 8263–67. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10641131.

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Xie, Ruobing, Zhiyuan Liu, Huanbo Luan e Maosong Sun. "Image-embodied Knowledge Representation Learning". In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/438.

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Entity images could provide significant visual information for knowledge representation learning. Most conventional methods learn knowledge representations merely from structured triples, ignoring rich visual information extracted from entity images. In this paper, we propose a novel Image-embodied Knowledge Representation Learning model (IKRL), where knowledge representations are learned with both triple facts and images. More specifically, we first construct representations for all images of an entity with a neural image encoder. These image representations are then integrated into an aggregated image-based representation via an attention-based method. We evaluate our IKRL models on knowledge graph completion and triple classification. Experimental results demonstrate that our models outperform all baselines on both tasks, which indicates the significance of visual information for knowledge representations and the capability of our models in learning knowledge representations with images.

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Li, Zechao. "Understanding-oriented visual representation learning". In the 7th International Conference. New York, New York, USA: ACM Press, 2015. http://dx.doi.org/10.1145/2808492.2808572.

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Lee, Donghun, Seonghyun Kim, Samyeul Noh, Heechul Bae e Ingook Jang. "High-level Visual Representation via Perceptual Representation Learning". In 2023 14th International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2023. http://dx.doi.org/10.1109/ictc58733.2023.10393558.

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Kolesnikov, Alexander, Xiaohua Zhai e Lucas Beyer. "Revisiting Self-Supervised Visual Representation Learning". In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.00202.

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Sariyildiz, Mert Bulent, Yannis Kalantidis, Diane Larlus e Karteek Alahari. "Concept Generalization in Visual Representation Learning". In 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2021. http://dx.doi.org/10.1109/iccv48922.2021.00949.

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Özçelİk, Timoteos Onur, Berk Gökberk e Lale Akarun. "Self-Supervised Dense Visual Representation Learning". In 2024 32nd Signal Processing and Communications Applications Conference (SIU). IEEE, 2024. http://dx.doi.org/10.1109/siu61531.2024.10600771.

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Broscheit, Samuel. "Learning Distributional Token Representations from Visual Features". In Proceedings of The Third Workshop on Representation Learning for NLP. Stroudsburg, PA, USA: Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/w18-3025.

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Hong, Xudong, Vera Demberg, Asad Sayeed, Qiankun Zheng e Bernt Schiele. "Visual Coherence Loss for Coherent and Visually Grounded Story Generation". In Proceedings of the 8th Workshop on Representation Learning for NLP (RepL4NLP 2023). Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.repl4nlp-1.27.

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Relatórios de organizações sobre o assunto "Visual representation learning"

Tarasenko, Rostyslav O., Svitlana M. Amelina, Yuliya M. Kazhan e Olga V. Bondarenko. The use of AR elements in the study of foreign languages at the university. CEUR Workshop Proceedings, novembro de 2020. http://dx.doi.org/10.31812/123456789/4421.

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The article deals with the analysis of the impact of the using AR technology in the study of a foreign language by university students. It is stated out that AR technology can be a good tool for learning a foreign language. The use of elements of AR in the course of studying a foreign language, in particular in the form of virtual excursions, is proposed. Advantages of using AR technology in the study of the German language are identified, namely: the possibility of involvement of different channels of information perception, the integrity of the representation of the studied object, the faster and better memorization of new vocabulary, the development of communicative foreign language skills. The ease and accessibility of using QR codes to obtain information about the object of study from open Internet sources is shown. The results of a survey of students after virtual tours are presented. A reorientation of methodological support for the study of a foreign language at universities is proposed. Attention is drawn to the use of AR elements in order to support students with different learning styles (audio, visual, kinesthetic).

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Shukla, Indu, Rajeev Agrawal, Kelly Ervin e Jonathan Boone. AI on digital twin of facility captured by reality scans. Engineer Research and Development Center (U.S.), novembro de 2023. http://dx.doi.org/10.21079/11681/47850.

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The power of artificial intelligence (AI) coupled with optimization algorithms can be linked to data-rich digital twin models to perform predictive analysis to make better informed decisions about installation operations and quality of life for the warfighters. In the current research, we developed AI connected lifecycle building information models through the creation of a data informed smart digital twin of one of US Army Corps of Engineers (USACE) buildings as our test case. Digital twin (DT) technology involves creating a virtual representation of a physical entity. Digital twin is created by digitalizing data collected through sensors, powered by machine learning (ML) algorithms, and are continuously learning systems. The exponential advance in digital technologies enables facility spaces to be fully and richly modeled in three dimensions and can be brought together in virtual space. Coupled with advancement in reinforcement learning and computer graphics enables AI agents to learn visual navigation and interaction with objects. We have used Habitat AI 2.0 to train an embodied agent in immersive 3D photorealistic environment. The embodied agent interacts with a 3D environment by receiving RGB, depth and semantically segmented views of the environment and taking navigational actions and interacts with the objects in the 3D space. Instead of training the robots in physical world we are training embodied agents in simulated 3D space. While humans are superior at critical thinking, creativity, and managing people, whereas robots are superior at coping with harsh environments and performing highly repetitive work. Training robots in controlled simulated world is faster and can increase their surveillance, reliability, efficiency, and survivability in physical space.

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Iatsyshyn, Anna V., Valeriia O. Kovach, Yevhen O. Romanenko, Iryna I. Deinega, Andrii V. Iatsyshyn, Oleksandr O. Popov, Yulii G. Kutsan, Volodymyr O. Artemchuk, Oleksandr Yu Burov e Svitlana H. Lytvynova. Application of augmented reality technologies for preparation of specialists of new technological era. [б. в.], fevereiro de 2020. http://dx.doi.org/10.31812/123456789/3749.

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Augmented reality is one of the most modern information visualization technologies. Number of scientific studies on different aspects of augmented reality technology development and application is analyzed in the research. Practical examples of augmented reality technologies for various industries are described. Very often augmented reality technologies are used for: social interaction (communication, entertainment and games); education; tourism; areas of purchase/sale and presentation. There are various scientific and mass events in Ukraine, as well as specialized training to promote augmented reality technologies. There are following results of the research: main benefits that educational institutions would receive from introduction of augmented reality technology are highlighted; it is determined that application of augmented reality technologies in education would contribute to these technologies development and therefore need increase for specialists in the augmented reality; growth of students' professional level due to application of augmented reality technologies is proved; adaptation features of augmented reality technologies in learning disciplines for students of different educational institutions are outlined; it is advisable to apply integrated approach in the process of preparing future professionals of new technological era; application of augmented reality technologies increases motivation to learn, increases level of information assimilation due to the variety and interactivity of its visual representation. Main difficulties of application of augmented reality technologies are financial, professional and methodical. Following factors are necessary for introduction of augmented reality technologies: state support for such projects and state procurement for development of augmented reality technologies; conduction of scientific research and experimental confirmation of effectiveness and pedagogical expediency of augmented reality technologies application for training of specialists of different specialties; systematic conduction of number of national and international events on dissemination and application of augmented reality technology. It is confirmed that application of augmented reality technologies is appropriate for training of future specialists of new technological era.

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Zerla, Pauline. Trauma, Violence Prevention, and Reintegration: Learning from Youth Conflict Narratives in the Central African Republic. RESOLVE Network, fevereiro de 2024. http://dx.doi.org/10.37805/lpbi2024.1.

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This research report is a case study on the relationship between trauma, peacebuilding, and reintegration for conflict-affected youth in the Central African Republic (CAR) following the 2019 peace agreement. Based on qualitative research fielded in Spring 2022, the study examines how youth experience conflict, trauma, and reintegration in CAR, highlighting individual experiences through a participant narrative approach. In doing so, the report provides localized insight into the challenges that impact social reintegration and cohesion in fragile, conflict-affected contexts. The report further underscores the implications of these insights for local and international efforts to establish peace and security through disarmament, demobilization, and reintegration (DDR) programs and community violence reduction (CVR) initiatives. In addition to standard data collection methods such as interviews and focus group discussions, data collection undertaken for this report utilized a trauma-informed method called body mapping. The use of body maps—life size images of a human body with visual representations of experiences— in research can offer a means for individuals to reflect on potentially difficult experiences through a non-verbal process. Given the potential relevance of this tool in future studies examining the nexus between conflict, reintegration, mental health, and trauma, this report also includes discussion of the implementation of this method with considerations for others hoping to adapt it for their own use.

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