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Author : Angela Eigenstetter
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (931 download)
Download or read book Learning Mid-level Representations for Visual Recognition written by Angela Eigenstetter and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Yang Zhang
Publisher :
ISBN 13 :
Total Pages : 128 pages
Book Rating : 4.:/5 (117 download)
Download or read book Learning Transferable Representations for Visual Recognition written by Yang Zhang and published by . This book was released on 2020 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the last half-decade, a new renaissance of machine learning originates from the applications of convolutional neural networks to visual recognition tasks. It is believed that a combination of big curated data and novel deep learning techniques can lead to unprecedented results. However, the increasingly large training data is still a drop in the ocean compared with scenarios in the wild. In this literature, we focus on learning transferable representation in the neural networks to ensure the models stay robust, even given different data distributions. We present three exemplar topics in three chapters, respectively: zero-shot learning, domain adaptation, and generalizable adversarial attack. By zero-shot learning, we enable models to predict labels not seen in the training phase. By domain adaptation, we improve a model’s performance on the target domain by mitigating its discrepancy from a labeled source model, without any target annotation. Finally, the generalization adversarial attack focuses on learning an adversarial camouflage that ideally would work in every possible scenario. Despite sharing the same transfer learning philosophy, each of the proposed topics poses a unique challenge requiring a unique solution. In each chapter, we introduce the problem as well as present our solution to the problem. We also discuss some other researchers’ approaches and compare our solution to theirs in the experiments.
Author : Kristen Thielscher
Publisher : Springer Nature
ISBN 13 : 3031015533
Total Pages : 163 pages
Book Rating : 4.0/5 (31 download)
Download or read book Visual Object Recognition written by Kristen Thielscher and published by Springer Nature. This book was released on 2022-05-31 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: The visual recognition problem is central to computer vision research. From robotics to information retrieval, many desired applications demand the ability to identify and localize categories, places, and objects. This tutorial overviews computer vision algorithms for visual object recognition and image classification. We introduce primary representations and learning approaches, with an emphasis on recent advances in the field. The target audience consists of researchers or students working in AI, robotics, or vision who would like to understand what methods and representations are available for these problems. This lecture summarizes what is and isn't possible to do reliably today, and overviews key concepts that could be employed in systems requiring visual categorization. Table of Contents: Introduction / Overview: Recognition of Specific Objects / Local Features: Detection and Description / Matching Local Features / Geometric Verification of Matched Features / Example Systems: Specific-Object Recognition / Overview: Recognition of Generic Object Categories / Representations for Object Categories / Generic Object Detection: Finding and Scoring Candidates / Learning Generic Object Category Models / Example Systems: Generic Object Recognition / Other Considerations and Current Challenges / Conclusions
Author : Hong Cheng
Publisher : Springer
ISBN 13 : 1447167147
Total Pages : 259 pages
Book Rating : 4.4/5 (471 download)
Download or read book Sparse Representation, Modeling and Learning in Visual Recognition written by Hong Cheng and published by Springer. This book was released on 2015-05-25 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: This unique text/reference presents a comprehensive review of the state of the art in sparse representations, modeling and learning. The book examines both the theoretical foundations and details of algorithm implementation, highlighting the practical application of compressed sensing research in visual recognition and computer vision. Topics and features: describes sparse recovery approaches, robust and efficient sparse representation, and large-scale visual recognition; covers feature representation and learning, sparsity induced similarity, and sparse representation and learning-based classifiers; discusses low-rank matrix approximation, graphical models in compressed sensing, collaborative representation-based classification, and high-dimensional nonlinear learning; includes appendices outlining additional computer programming resources, and explaining the essential mathematics required to understand the book.
Author : Tonmoy Saikia
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)
Download or read book Learning Representations for Visual Recognition and Correspondence Tasks written by Tonmoy Saikia and published by . This book was released on 2023* with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Shimon Edelman
Publisher : MIT Press
ISBN 13 : 9780262050579
Total Pages : 378 pages
Book Rating : 4.0/5 (55 download)
Download or read book Representation and Recognition in Vision written by Shimon Edelman and published by MIT Press. This book was released on 1999 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: Shimon Edelman bases a comprehensive approach to visual representation on the notion of correspondence between proximal (internal) and distal similarities in objects. Researchers have long sought to understand what the brain does when we see an object, what two people have in common when they see the same object, and what a "seeing" machine would need to have in common with a human visual system. Recent neurobiological and computational advances in the study of vision have now brought us close to answering these and other questions about representation. In Representation and Recognition in Vision, Shimon Edelman bases a comprehensive approach to visual representation on the notion of correspondence between proximal (internal) and distal similarities in objects. This leads to a computationally feasible and formally veridical representation of distal objects that addresses the needs of shape categorization and can be used to derive models of perceived similarity. Edelman first discusses the representational needs of various visual recognition tasks, and surveys current theories of representation in this context. He then develops a theory of representation that is related to Shepard's notion of second-order isomorphism between representations and their targets. Edelman goes beyond Shepard by specifying the conditions under which the representations can be made formally veridical. Edelman assesses his theory's performance in identification and categorization of 3D shapes and examines it in light of psychological and neurobiological data concerning the object-processing stream in primate vision. He also discusses the connections between his theory and other efforts to understand representation in the brain.
Author : Rongrong Ji
Publisher : Morgan Kaufmann
ISBN 13 : 0128026200
Total Pages : 128 pages
Book Rating : 4.1/5 (28 download)
Download or read book Learning-Based Local Visual Representation and Indexing written by Rongrong Ji and published by Morgan Kaufmann. This book was released on 2015-04-08 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning-Based Local Visual Representation and Indexing, reviews the state-of-the-art in visual content representation and indexing, introduces cutting-edge techniques in learning based visual representation, and discusses emerging topics in visual local representation, and introduces the most recent advances in content-based visual search techniques. Discusses state-of-the-art procedures in learning-based local visual representation. Shows how to master the basic techniques needed for building a large-scale visual search engine and indexing system Provides insight into how machine learning techniques can be leveraged to refine the visual recognition system, especially in the part of visual feature representation.
Author : Walter J. Scheirer
Publisher : Springer Nature
ISBN 13 : 3031018176
Total Pages : 115 pages
Book Rating : 4.0/5 (31 download)
Download or read book Extreme Value Theory-Based Methods for Visual Recognition written by Walter J. Scheirer and published by Springer Nature. This book was released on 2022-06-01 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: A common feature of many approaches to modeling sensory statistics is an emphasis on capturing the "average." From early representations in the brain, to highly abstracted class categories in machine learning for classification tasks, central-tendency models based on the Gaussian distribution are a seemingly natural and obvious choice for modeling sensory data. However, insights from neuroscience, psychology, and computer vision suggest an alternate strategy: preferentially focusing representational resources on the extremes of the distribution of sensory inputs. The notion of treating extrema near a decision boundary as features is not necessarily new, but a comprehensive statistical theory of recognition based on extrema is only now just emerging in the computer vision literature. This book begins by introducing the statistical Extreme Value Theory (EVT) for visual recognition. In contrast to central-tendency modeling, it is hypothesized that distributions near decision boundaries form a more powerful model for recognition tasks by focusing coding resources on data that are arguably the most diagnostic features. EVT has several important properties: strong statistical grounding, better modeling accuracy near decision boundaries than Gaussian modeling, the ability to model asymmetric decision boundaries, and accurate prediction of the probability of an event beyond our experience. The second part of the book uses the theory to describe a new class of machine learning algorithms for decision making that are a measurable advance beyond the state-of-the-art. This includes methods for post-recognition score analysis, information fusion, multi-attribute spaces, and calibration of supervised machine learning algorithms.
Author : Farzad Siyahjani
Publisher :
ISBN 13 :
Total Pages : 141 pages
Book Rating : 4.:/5 (999 download)
Download or read book Subspace Representations and Learning for Visual Recognition written by Farzad Siyahjani and published by . This book was released on 2017 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Bastian Leibe
Publisher : Springer
ISBN 13 : 3319464841
Total Pages : 851 pages
Book Rating : 4.3/5 (194 download)
Download or read book Computer Vision – ECCV 2016 written by Bastian Leibe and published by Springer. This book was released on 2016-09-16 with total page 851 pages. Available in PDF, EPUB and Kindle. Book excerpt: The eight-volume set comprising LNCS volumes 9905-9912 constitutes the refereed proceedings of the 14th European Conference on Computer Vision, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016. The 415 revised papers presented were carefully reviewed and selected from 1480 submissions. The papers cover all aspects of computer vision and pattern recognition such as 3D computer vision; computational photography, sensing and display; face and gesture; low-level vision and image processing; motion and tracking; optimization methods; physics-based vision, photometry and shape-from-X; recognition: detection, categorization, indexing, matching; segmentation, grouping and shape representation; statistical methods and learning; video: events, activities and surveillance; applications. They are organized in topical sections on detection, recognition and retrieval; scene understanding; optimization; image and video processing; learning; action, activity and tracking; 3D; and 9 poster sessions.
Author : Jens Spehr
Publisher : Springer
ISBN 13 : 3319113259
Total Pages : 210 pages
Book Rating : 4.3/5 (191 download)
Download or read book On Hierarchical Models for Visual Recognition and Learning of Objects, Scenes, and Activities written by Jens Spehr and published by Springer. This book was released on 2014-11-13 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: In many computer vision applications, objects have to be learned and recognized in images or image sequences. This book presents new probabilistic hierarchical models that allow an efficient representation of multiple objects of different categories, scales, rotations, and views. The idea is to exploit similarities between objects and object parts in order to share calculations and avoid redundant information. Furthermore inference approaches for fast and robust detection are presented. These new approaches combine the idea of compositional and similarity hierarchies and overcome limitations of previous methods. Besides classical object recognition the book shows the use for detection of human poses in a project for gait analysis. The use of activity detection is presented for the design of environments for ageing, to identify activities and behavior patterns in smart homes. In a presented project for parking spot detection using an intelligent vehicle, the proposed approaches are used to hierarchically model the environment of the vehicle for an efficient and robust interpretation of the scene in real-time.
Author : Weijian Xu
Publisher :
ISBN 13 :
Total Pages : 127 pages
Book Rating : 4.:/5 (13 download)
Download or read book Exploring Visual Structures in Deep Representation Learning written by Weijian Xu and published by . This book was released on 2022 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep representation learning has dominated almost every task in computer vision and achieves superior performance. In deep representation learning, deep neural networks are trained on massive data to provide rich visual representations. However, general-purpose neural networks are not fully aware of visual structures, which limit their generalizability on specific vision tasks (e.g., skeleton detection and line segment detection) or under particular scenarios (e.g., few-shot settings). To tackle the aforementioned limitation, we find it natural and essential to enhance the deep representation with visual structures. This dissertation concentrates on three visual structures: geometric structure, part structure, and multi-scale structure. We then present four examples to study these visual structures in deep representation learning. First, we focus on object skeleton detection and introduce geometric structure in objective function design. Second, we encode part structure in a convolutional neural network for the few-shot image classification. Third, we build a generic vision Transformer with a co-scale structure for image recognition and instance-level prediction. Finally, we present a Transformer model with a multi-scale structure in line segment detection.
Author : Antonio Rodríguez-Sánchez
Publisher : Frontiers Media SA
ISBN 13 : 2889197980
Total Pages : 292 pages
Book Rating : 4.8/5 (891 download)
Download or read book Hierarchical Object Representations in the Visual Cortex and Computer Vision written by Antonio Rodríguez-Sánchez and published by Frontiers Media SA. This book was released on 2016-06-08 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the past 40 years, neurobiology and computational neuroscience has proved that deeper understanding of visual processes in humans and non-human primates can lead to important advancements in computational perception theories and systems. One of the main difficulties that arises when designing automatic vision systems is developing a mechanism that can recognize - or simply find - an object when faced with all the possible variations that may occur in a natural scene, with the ease of the primate visual system. The area of the brain in primates that is dedicated at analyzing visual information is the visual cortex. The visual cortex performs a wide variety of complex tasks by means of simple operations. These seemingly simple operations are applied to several layers of neurons organized into a hierarchy, the layers representing increasingly complex, abstract intermediate processing stages. In this Research Topic we propose to bring together current efforts in neurophysiology and computer vision in order 1) To understand how the visual cortex encodes an object from a starting point where neurons respond to lines, bars or edges to the representation of an object at the top of the hierarchy that is invariant to illumination, size, location, viewpoint, rotation and robust to occlusions and clutter; and 2) How the design of automatic vision systems benefit from that knowledge to get closer to human accuracy, efficiency and robustness to variations.
Author : Yongzhen Huang
Publisher : Springer
ISBN 13 : 3662450003
Total Pages : 80 pages
Book Rating : 4.6/5 (624 download)
Download or read book Feature Coding for Image Representation and Recognition written by Yongzhen Huang and published by Springer. This book was released on 2015-01-05 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt: This brief presents a comprehensive introduction to feature coding, which serves as a key module for the typical object recognition pipeline. The text offers a rich blend of theory and practice while reflects the recent developments on feature coding, covering the following five aspects: (1) Review the state-of-the-art, analyzing the motivations and mathematical representations of various feature coding methods; (2) Explore how various feature coding algorithms evolve along years; (3) Summarize the main characteristics of typical feature coding algorithms and categorize them accordingly; (4) Discuss the applications of feature coding in different visual tasks, analyze the influence of some key factors in feature coding with intensive experimental studies; (5) Provide the suggestions of how to apply different feature coding methods and forecast the potential directions for future work on the topic. It is suitable for students, researchers, practitioners interested in object recognition.
Author : Avadhesh Kumar
Publisher : CRC Press
ISBN 13 : 1000484211
Total Pages : 232 pages
Book Rating : 4.0/5 (4 download)
Download or read book Prediction and Analysis for Knowledge Representation and Machine Learning written by Avadhesh Kumar and published by CRC Press. This book was released on 2022-01-31 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: A number of approaches are being defined for statistics and machine learning. These approaches are used for the identification of the process of the system and the models created from the system’s perceived data, assisting scientists in the generation or refinement of current models. Machine learning is being studied extensively in science, particularly in bioinformatics, economics, social sciences, ecology, and climate science, but learning from data individually needs to be researched more for complex scenarios. Advanced knowledge representation approaches that can capture structural and process properties are necessary to provide meaningful knowledge to machine learning algorithms. It has a significant impact on comprehending difficult scientific problems. Prediction and Analysis for Knowledge Representation and Machine Learning demonstrates various knowledge representation and machine learning methodologies and architectures that will be active in the research field. The approaches are reviewed with real-life examples from a wide range of research topics. An understanding of a number of techniques and algorithms that are implemented in knowledge representation in machine learning is available through the book’s website. Features: Examines the representational adequacy of needed knowledge representation Manipulates inferential adequacy for knowledge representation in order to produce new knowledge derived from the original information Improves inferential and acquisition efficiency by applying automatic methods to acquire new knowledge Covers the major challenges, concerns, and breakthroughs in knowledge representation and machine learning using the most up-to-date technology Describes the ideas of knowledge representation and related technologies, as well as their applications, in order to help humankind become better and smarter This book serves as a reference book for researchers and practitioners who are working in the field of information technology and computer science in knowledge representation and machine learning for both basic and advanced concepts. Nowadays, it has become essential to develop adaptive, robust, scalable, and reliable applications and also design solutions for day-to-day problems. The edited book will be helpful for industry people and will also help beginners as well as high-level users for learning the latest things, which include both basic and advanced concepts.
Author : Kristen Grauman
Publisher : Morgan & Claypool Publishers
ISBN 13 : 1598299689
Total Pages : 184 pages
Book Rating : 4.5/5 (982 download)
Download or read book Visual Object Recognition written by Kristen Grauman and published by Morgan & Claypool Publishers. This book was released on 2011 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: The visual recognition problem is central to computer vision research. From robotics to information retrieval, many desired applications demand the ability to identify and localize categories, places, and objects. This tutorial overviews computer vision algorithms for visual object recognition and image classification. We introduce primary representations and learning approaches, with an emphasis on recent advances in the field. The target audience consists of researchers or students working in AI, robotics, or vision who would like to understand what methods and representations are available for these problems. This lecture summarizes what is and isn't possible to do reliably today, and overviews key concepts that could be employed in systems requiring visual categorization. Table of Contents: Introduction / Overview: Recognition of Specific Objects / Local Features: Detection and Description / Matching Local Features / Geometric Verification of Matched Features / Example Systems: Specific-Object Recognition / Overview: Recognition of Generic Object Categories / Representations for Object Categories / Generic Object Detection: Finding and Scoring Candidates / Learning Generic Object Category Models / Example Systems: Generic Object Recognition / Other Considerations and Current Challenges / Conclusions
Author : Bolei Zhou
Publisher :
ISBN 13 :
Total Pages : 140 pages
Book Rating : 4.:/5 (15 download)
Download or read book Interpretable Representation Learning for Visual Intelligence written by Bolei Zhou and published by . This book was released on 2018 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
