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Domain Adaptation for Visual Recognition

A comprehensive overview of domain adaptation solutions for visual recognition problems.

Author : Raghuraman Gopalan

Release : 2015-03-26

Publisher : Now Publishers

ISBN : 9781680830309

File Size : 69.51 MB

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This monograph provides a comprehensive overview of domain adaptation solutions for visual recognition problems. It discusses the existing adaptation techniques in the literature, which are motivated by the principles of max-margin discriminative learning, manifold learning, sparse coding, as well as low-rank representations. It also analyzes the challenges posed by the realm of "big visual data" in terms of the generalization ability of adaptation algorithms to unconstrained data acquisition as well as issues related to their computational tractability

Domain Adaptation for Visual Understanding

This unique volume reviews the latest advances in domain adaptation in the training of machine learning algorithms for visual understanding, offering valuable insights from an international selection of experts in the field.

Author : Richa Singh

Release : 2020-01-08

Publisher : Springer Nature

ISBN : 3030306712

File Size : 84.53 MB

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This unique volume reviews the latest advances in domain adaptation in the training of machine learning algorithms for visual understanding, offering valuable insights from an international selection of experts in the field. The text presents a diverse selection of novel techniques, covering applications of object recognition, face recognition, and action and event recognition. Topics and features: reviews the domain adaptation-based machine learning algorithms available for visual understanding, and provides a deep metric learning approach; introduces a novel unsupervised method for image-to-image translation, and a video segment retrieval model that utilizes ensemble learning; proposes a unique way to determine which dataset is most useful in the base training, in order to improve the transferability of deep neural networks; describes a quantitative method for estimating the discrepancy between the source and target data to enhance image classification performance; presents a technique for multi-modal fusion that enhances facial action recognition, and a framework for intuition learning in domain adaptation; examines an original interpolation-based approach to address the issue of tracking model degradation in correlation filter-based methods. This authoritative work will serve as an invaluable reference for researchers and practitioners interested in machine learning-based visual recognition and understanding.

Domain Adaptation in Computer Vision Applications

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
... Unsupervised domain adaptation by backpropagation. ... Learning kernels for
unsupervised domain adaptation with applications to visual object recognition.

Author : Gabriela Csurka

Release : 2017-09-10

Publisher : Springer

ISBN : 3319583476

File Size : 49.38 MB

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This comprehensive text/reference presents a broad review of diverse domain adaptation (DA) methods for machine learning, with a focus on solutions for visual applications. The book collects together solutions and perspectives proposed by an international selection of pre-eminent experts in the field, addressing not only classical image categorization, but also other computer vision tasks such as detection, segmentation and visual attributes. Topics and features: surveys the complete field of visual DA, including shallow methods designed for homogeneous and heterogeneous data as well as deep architectures; presents a positioning of the dataset bias in the CNN-based feature arena; proposes detailed analyses of popular shallow methods that addresses landmark data selection, kernel embedding, feature alignment, joint feature transformation and classifier adaptation, or the case of limited access to the source data; discusses more recent deep DA methods, including discrepancy-based adaptation networks and adversarial discriminative DA models; addresses domain adaptation problems beyond image categorization, such as a Fisher encoding adaptation for vehicle re-identification, semantic segmentation and detection trained on synthetic images, and domain generalization for semantic part detection; describes a multi-source domain generalization technique for visual attributes and a unifying framework for multi-domain and multi-task learning. This authoritative volume will be of great interest to a broad audience ranging from researchers and practitioners, to students involved in computer vision, pattern recognition and machine learning.

Self-supervised Learning and Domain Adaptation for Visual Analysis

We propose a novel approach to learn the human mesh representation without any ground truth mesh. This is made possible by introducing two new terms into the loss function of a graph convolutional neural network (Graph CNN).

Author : Kevin Lin

Release : 2020

Publisher :

ISBN :

File Size : 78.74 MB

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Supervised training with deep Convolutional Neural Networks (CNNs) have achieved great success in various visual recognition tasks. However, supervised training with deep CNNs requires large amount of well-annotated data. Data labeling, especially for large-scale image dataset, is very expensive. How to learn an effective model without the need of training data labeling has become an important problem for many applications. A promising solution is to create a learning protocol for the neural networks, so that the neural networks can learn to teach itself without manual labels. This technique is referred as the self-supervised learning, which has recently drawn an increasing attention for improving the learning performance. In this thesis, we first present our work on learning binary descriptors for fast image retrieval without manual labeling. We observe that images with the same category should have similar visual textures, and these similar textures are usually invariant to shift, scale and rotation. Thus, we could generate similar texture patch pairs automatically for training CNNs by shifting, scaling, and rotating image patches. Based on the observation, we design a training protocol for deep CNNs, which automatically generates pair-wise pseudo labels describing the similarity between the given two images. The proposed method performs more favorably than the baselines on different tasks including patch matching, image retrieval, and object recognition. In the second part of this thesis, we turn our focus to the task of human-centric analysis applications, and present our work on learning multi-person part segmentation without human labeling. Our proposed complementary learning technique learns a neural network model for multi-person part segmentation using a synthetic dataset and a real dataset. We observe that real and synthetic humans share a common skeleton structure. During learning, the proposed model extracts human skeletons which effectively bridges the synthetic and real domains. Without using human-annotated part segmentation labels, the resultant model works well on real world images. Our method outperforms the state-of-the-art approaches on multiple public datasets. Then, we discuss our work on accelerating multi-person pose estimation using a proposed concatenated pyramid network. We observe that each image may contain an unknown number of people that can occur at any scale or position. This makes fast multi-person pose estimation very challenging. Different from the earlier deep learning approaches that extract image features by using a series of convolutions, our proposed method extracts image features from each convolution layer in parallel, which better captures image features in different scales and improve the performance of human pose estimation. Our proposed method eliminates the need of multi-scale inference and multi-stage detection, and the proposed method is many times faster than the state-of-the-art approaches, while achieving better accuracy on the public datasets. Next, we present our work on 3D human mesh construction from a single image. We propose a novel approach to learn the human mesh representation without any ground truth mesh. This is made possible by introducing two new terms into the loss function of a graph convolutional neural network (Graph CNN). The first term is the Laplacian prior that acts as a regularizer on the mesh construction. The second term is the part segmentation loss that forces the projected region of the constructed mesh to match the part segmentation. Experimental results on multiple public datasets show that without using 3D ground truth meshes, the proposed approach outperforms the previous state-of-the-art approaches that require 3D ground truth meshes for training. Finally, we summarize our completed works and discuss the future research directions.

Domain Adaptation in Computer Vision with Deep Learning

The second part of this book highlights feature alignment based approaches to domain adaptation. The third part of this book outlines image alignment procedures for domain adaptation.

Author : Hemanth Venkateswara

Release :

Publisher : Springer Nature

ISBN : 3030455297

File Size : 24.78 MB

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Intelligence Science and Big Data Engineering. Big Data and Machine Learning

Gopalan, R., Li, R., Chellappa, R.: Domain adaptation for object recognition: an
unsupervised approach. In: ICCV (2011) Griffin, G.S., Holub, A.D., Perona, P:
Caltech-256 object category dataset. California Institute of Technology (2007)
Kan, ...

Author : Zhen Cui

Release : 2019-11-28

Publisher : Springer Nature

ISBN : 3030362043

File Size : 79.58 MB

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The two volumes LNCS 11935 and 11936 constitute the proceedings of the 9th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2019, held in Nanjing, China, in October 2019. The 84 full papers presented were carefully reviewed and selected from 252 submissions.The papers are organized in two parts: visual data engineering; and big data and machine learning. They cover a large range of topics including information theoretic and Bayesian approaches, probabilistic graphical models, big data analysis, neural networks and neuro-informatics, bioinformatics, computational biology and brain-computer interfaces, as well as advances in fundamental pattern recognition techniques relevant to image processing, computer vision and machine learning.

Learning Transferable Representations for Visual Recognition

In the last half-decade, a new renaissance of machine learning originates from the applications of convolutional neural networks to visual recognition tasks.

Author : Yang Zhang

Release : 2020

Publisher :

ISBN :

File Size : 60.60 MB

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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.

Low-Rank and Sparse Modeling for Visual Analysis

Low-Rank Transfer Learning Ming Shao, Dmitry Kit and Yun Fu Abstract Real-
world visual data are expensive to label for ... important computer vision
problems, e.g., face recognition application, visual domain adaptation for object
recognition, ...

Author : Yun Fu

Release : 2014-10-30

Publisher : Springer

ISBN : 331912000X

File Size : 27.86 MB

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This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding and learning among unconstrained visual data. The book includes chapters covering multiple emerging topics in this new field. It links multiple popular research fields in Human-Centered Computing, Social Media, Image Classification, Pattern Recognition, Computer Vision, Big Data, and Human-Computer Interaction. Contains an overview of the low-rank and sparse modeling techniques for visual analysis by examining both theoretical analysis and real-world applications.

Pattern Recognition

Deng, J., Dong, W., Socher, R., Li, L., Li, K., Fei-Fei, L.: ImageNet: a large-scale
hierarchical image database. ... 106(1), 59–70 (2007) Fernando, B., Habrard, A.,
Sebban, M., Tuytelaars, T.: Unsupervised visual domain adaptation using ...

Author : Juergen Gall

Release : 2015-10-06

Publisher : Springer

ISBN : 3319249479

File Size : 55.90 MB

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This book constitutes the refereed proceedings of the 37th German Conference on Pattern Recognition, GCPR 2015, held in Aachen, Germany, in October 2015. The 45 revised full papers and one Young Researchers Forum presented were carefully reviewed and selected from 108 submissions. The papers are organized in topical sections on motion and reconstruction; mathematical foundations and image processing; biomedical image analysis and applications; human pose analysis; recognition and scene understanding.

Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data

... Deep residual learning for image recognition. CoRR abs/1512.03385 (2015).
http://arxiv.org/abs/1512.03385 2. He, K., Zhang, X., Ren, S., Sun, J.: Delving
deep into rectifiers: surpassing humanlevel performance on imagenet
classification.

Author : Qian Wang

Release : 2019-12-16

Publisher : Springer Nature

ISBN : 3030333914

File Size : 85.14 MB

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This book constitutes the refereed proceedings of the First MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2019, and the First International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. DART 2019 accepted 12 papers for publication out of 18 submissions. The papers deal with methodological advancements and ideas that can improve the applicability of machine learning and deep learning approaches to clinical settings by making them robust and consistent across different domains. MIL3ID accepted 16 papers out of 43 submissions for publication, dealing with best practices in medical image learning with label scarcity and data imperfection.

Computer Vision -- ECCV 2010

Domain adaptation is an important emerging topic in computer vision. In this
paper, we present one of the first studies of domain shift in the context of object
recognition. We introduce a method that adapts object models acquired in a
particular ...

Author : Kostas Daniilidis

Release : 2010-08-30

Publisher : Springer Science & Business Media

ISBN : 364215560X

File Size : 85.81 MB

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The six-volume set comprising LNCS volumes 6311 until 6313 constitutes the refereed proceedings of the 11th European Conference on Computer Vision, ECCV 2010, held in Heraklion, Crete, Greece, in September 2010. The 325 revised papers presented were carefully reviewed and selected from 1174 submissions. The papers are organized in topical sections on object and scene recognition; segmentation and grouping; face, gesture, biometrics; motion and tracking; statistical models and visual learning; matching, registration, alignment; computational imaging; multi-view geometry; image features; video and event characterization; shape representation and recognition; stereo; reflectance, illumination, color; medical image analysis.

Pattern Recognition and Computer Vision

The three-volume set LNCS 11857, 11858, and 11859 constitutes the refereed proceedings of the Second Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019, held in Xi’an, China, in November 2019.

Author : Zhouchen Lin

Release : 2019-10-31

Publisher : Springer Nature

ISBN : 3030316548

File Size : 67.51 MB

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The three-volume set LNCS 11857, 11858, and 11859 constitutes the refereed proceedings of the Second Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019, held in Xi’an, China, in November 2019. The 165 revised full papers presented were carefully reviewed and selected from 412 submissions. The papers have been organized in the following topical sections: Part I: Object Detection, Tracking and Recognition, Part II: Image/Video Processing and Analysis, Part III: Data Analysis and Optimization.

Pattern Recognition and Image Analysis

This 2-volume set constitutes the refereed proceedings of the 9th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2019, held in Madrid, Spain, in July 2019.

Author : Aythami Morales

Release : 2019-09-21

Publisher : Springer Nature

ISBN : 3030313328

File Size : 43.37 MB

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This 2-volume set constitutes the refereed proceedings of the 9th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2019, held in Madrid, Spain, in July 2019. The 99 papers in these volumes were carefully reviewed and selected from 137 submissions. They are organized in topical sections named: Part I: best ranked papers; machine learning; pattern recognition; image processing and representation. Part II: biometrics; handwriting and document analysis; other applications.

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

This book constitutes the refereed post-conference proceedings of the 23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018, held in Madrid, Spain, in November 2018 The 112 papers presented were carefully reviewed and selected from ...

Author : Ruben Vera-Rodriguez

Release : 2019-03-02

Publisher : Springer

ISBN : 3030134695

File Size : 34.46 MB

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This book constitutes the refereed post-conference proceedings of the 23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018, held in Madrid, Spain, in November 2018 The 112 papers presented were carefully reviewed and selected from 187 submissions The program was comprised of 6 oral sessions on the following topics: machine learning, computer vision, classification, biometrics and medical applications, and brain signals, and also on: text and character analysis, human interaction, and sentiment analysis

Computer Vision – ACCV 2018

The six volume set LNCS 11361-11366 constitutes the proceedings of the 14th Asian Conference on Computer Vision, ACCV 2018, held in Perth, Australia, in December 2018.

Author : C. V. Jawahar

Release : 2019-05-28

Publisher : Springer

ISBN : 3030208931

File Size : 22.89 MB

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The six volume set LNCS 11361-11366 constitutes the proceedings of the 14th Asian Conference on Computer Vision, ACCV 2018, held in Perth, Australia, in December 2018. The total of 274 contributions was carefully reviewed and selected from 979 submissions during two rounds of reviewing and improvement. The papers focus on motion and tracking, segmentation and grouping, image-based modeling, dep learning, object recognition object recognition, object detection and categorization, vision and language, video analysis and event recognition, face and gesture analysis, statistical methods and learning, performance evaluation, medical image analysis, document analysis, optimization methods, RGBD and depth camera processing, robotic vision, applications of computer vision.

Theory and Algorithms for Hypothesis Transfer Learning

Mots-clés de l'auteur: transfer learning ; domain adaptation ; statistical learning theory ; stochastic optimization ; visual recognition.

Author : Ilja Kuzborskij

Release : 2018

Publisher :

ISBN :

File Size : 34.64 MB

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Mots-clés de l'auteur: transfer learning ; domain adaptation ; statistical learning theory ; stochastic optimization ; visual recognition.

Pattern Recognition and Computer Vision

The three-volume set LNCS 12305, 12306, and 12307 constitutes the refereed proceedings of the Third Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2020, held virtually in Nanjing, China, in October 2020.

Author : Yuxin Peng

Release : 2020-10-11

Publisher : Springer Nature

ISBN : 3030606333

File Size : 55.94 MB

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The three-volume set LNCS 12305, 12306, and 12307 constitutes the refereed proceedings of the Third Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2020, held virtually in Nanjing, China, in October 2020. The 158 full papers presented were carefully reviewed and selected from 402 submissions. The papers have been organized in the following topical sections: Part I: Computer Vision and Application, Part II: Pattern Recognition and Application, Part III: Machine Learning.

Image Analysis and Processing – ICIAP 2019

The two-volume set LNCS 11751 and 11752 constitutes the refereed proceedings of the 20th International Conference on Image Analysis and Processing, ICIAP 2019, held in Trento, Italy, in September 2019.

Author : Elisa Ricci

Release : 2019-11-03

Publisher : Springer Nature

ISBN : 3030306453

File Size : 59.44 MB

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The two-volume set LNCS 11751 and 11752 constitutes the refereed proceedings of the 20th International Conference on Image Analysis and Processing, ICIAP 2019, held in Trento, Italy, in September 2019. The 117 papers presented were carefully reviewed and selected from 207 submissions. The papers cover both classic and the most recent trends in image processing, computer vision, and pattern recognition, addressing both theoretical and applicative aspects. They are organized in the following topical sections: Video Analysis and Understanding; Pattern Recognition and Machine Learning; Deep Learning; Multiview Geometry and 3D Computer Vision; Image Analysis, Detection and Recognition; Multimedia; Biomedical and Assistive Technology; Digital Forensics; Image processing for Cultural Heritage.

Pattern Recognition And Big Data

Containing twenty six contributions by experts from all over the world, this book presents both research and review material describing the evolution and recent developments of various pattern recognition methodologies, ranging from ...

Author : Pal Sankar Kumar

Release : 2016-12-15

Publisher : World Scientific

ISBN : 9813144564

File Size : 84.28 MB

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Containing twenty six contributions by experts from all over the world, this book presents both research and review material describing the evolution and recent developments of various pattern recognition methodologies, ranging from statistical, linguistic, fuzzy-set-theoretic, neural, evolutionary computing and rough-set-theoretic to hybrid soft computing, with significant real-life applications. Pattern Recognition and Big Data provides state-of-the-art classical and modern approaches to pattern recognition and mining, with extensive real life applications. The book describes efficient soft and robust machine learning algorithms and granular computing techniques for data mining and knowledge discovery; and the issues associated with handling Big Data. Application domains considered include bioinformatics, cognitive machines (or machine mind developments), biometrics, computer vision, the e-nose, remote sensing and social network analysis.

Computer Vision – ECCV 2018

The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.The 776 revised papers presented ...

Author : Vittorio Ferrari

Release : 2018-10-06

Publisher : Springer

ISBN : 3030012700

File Size : 34.60 MB

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The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.