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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wang, Tao Du, Maggie Wu, Xinmin He, Taiping SAS Inst Inc Cary NC 27513 USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Machine learning algorithms often use data from databases that are mutable;therefore, the data and the results of machine learning cannot be fully trusted. Also, the learning process is often difficult to automate. A unified analytical framework for trusted machine learning has been presented in the literature to address both issues. It is proposed building a trusted machine learning system by using blockchain technology, which can store data in a permanent and immutable way. In addition, smart contracts on blockchain are used to automate the machine learning process. However, in such a blockchain framework, data efficiency is a big concern, because it is very expensive to store a large amount of data on blockchain. On the other hand, machine learning based computer vision systems often rely on a lot of data. Therefore, to fully leverage a blockchain-based machine learning framework for computer vision systems, data efficiency issues must be addressed. This paper investigates how to enhance data efficiency in such a framework to bring computer vision systems to the edge. It presents a three-step approach. First, a lightweight machine learning model is trained on the server layer. Second, the trained model is saved in a special binary data format for data efficiency. Finally, the streaming layer takes these binary data as input and scores incoming new data in an online fashion. Real-time semantic segmentation for autonomous driving is used as an example to demonstrate how this approach works. This paper makes the following contributions. First, it improves the analytical framework for trusted computer vision systems based on blockchain. Second, the real-time semantic segmentation example shows how data-efficient learning for computer vision can be performed on the edge.

关键词： Machine learning computer vision Computational modeling Servers Contracts Automation

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Foreword to the Special Issue on computer vision-Based Approaches for Earth Observation

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ieee JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2020年 13卷 6515-6518页

作者： Tuia, Devis Hansch, Ronny Yokoya, Naoto Brown, Myron Le Saux, Bertrand EPFL Valais CH-1950 Sion Switzerland German Aerosp Ctr D-82234 Wessling Germany Univ Tokyo Tokyo 1138654 Japan RIKEN Ctr Adv Intelligence Project Tokyo 1030027 Japan Johns Hopkins Univ Appl Phys Lab Laurel MD 11100 USA European Space Res Inst & Lab European Space Agcy I-00044 Frascati Italy

The five papers in this special section focus on computer vision-based approaches for Earth observation. These papers followed a series of events promoting works at the interface between computer vision and remote sensing: the special sessions organized at the Living Planet Symposium1 and the computer vision and pattern recognition (cvpr) conference (the Earthvision2 and computer vision for Global Challenges3 workshops). These sessions aimed at fostering collaboration between the computer vision and earth observation communities to boost automated interpretation of remotely sensed data. They also aimed at raising awareness inside the computer vision community for this highly challenging and quickly evolving field of research with a big impact on human society, economy, industry, and the planet. Submissions were invited from all areas of computer vision and image analysis relevant for, or applied to environmental remote sensing and were not limited to the papers presented at the events above. The papers retained in this special issue reflect the high variety of automatic image analysis in remote sensing.

关键词： Special issues and sections Remote sensing computer vision Satellites Hyperspectral images Synthetic aperture radar

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Densely Self-guided Wavelet Network for Image Denoising

Densely Self-guided Wavelet Network for Image Denoising

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Liu, Wei Yan, Qiong Zhao, Yuzhi SenseTime Res Hong Kong Peoples R China Harbin Inst Technol Harbin Heilongjiang Peoples R China City Univ Hong Kong Hong Kong Peoples R China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

During the past years, deep convolutional neural networks have achieved impressive success in image denoising. In this paper, we propose a densely self-guided wavelet network (DSWN) for real-world image denoising. The basic structure of DSWN is a top-down self-guidance architecture which is able to efficiently incorporate multi- scale information and extract good local features to recover clean images. Moreover, such a structure requires a smaller number of parameters and enables us to achieve better effectiveness than Unet structure. To avoid information loss and achieve a better receptive field size, we embed wavelet transform into DSWN. In addition, we apply densely residual learning to convolution blocks to enhance the feature extraction capability of the proposed network. At the full resolution level of DSWN, we adopt a double branch structure to generate the final output. One branch of them tends to pay attention to dark areas and the other performs better on bright areas. Such a double branch strategy is able to handle the noise at different exposures. The proposed network is validated by BSD68, Kodak24 and SIDD+ benchmark. Additional experimental results show that the proposed network outperforms most state-of-the-art image denoising solutions.

关键词： computer vision pattern recognition conferences

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Bilinear Parameterization For Differentiable Rank-Regularization

Bilinear Parameterization For Differentiable Rank-Regulariza...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Ornhag, Marcus Valtonen Olsson, Carl Heyden, Anders Lund Univ Ctr Math Sci Lund Sweden Chalmers Univ Technol Dept Elect Engn Gothenburg Sweden

ISBN: (纸本)9781728193601

Low rank approximation is a commonly occurring problem in many computer vision and machine learning applications. There are two common ways of optimizing the resulting models. Either the set of matrices with a given rank can be explicitly parametrized using a bilinear factorization, or low rank can be implicitly enforced using regularization terms penalizing non-zero singular values. While the former approach results in differentiable problems that can be efficiently optimized using local quadratic approximation, the latter is typically not differentiable (sometimes even discontinuous) and requires first order subgradient or splitting methods. It is well known that gradient based methods exhibit slow convergence for ill-conditioned problems. In this paper we show how many non-differentiable regularization methods can be reformulated into smooth objectives using bilinear parameterization. This allows us to use standard second order methods, such as Levenberg-Marquardt (LM) and Variable Projection (VarPro), to achieve accurate solutions for ill-conditioned cases. We show on several real and synthetic experiments that our second order formulation converges to substantially more accurate solutions than competing state-of-the-art methods.(1)

关键词： computer vision

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DNDNet: Reconfiguring CNN for Adversarial Robustness

DNDNet: Reconfiguring CNN for Adversarial Robustness

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Goel, Akhil Agarwal, Akshay Vatsa, Mayank Singh, Richa Ratha, Nalini K. IIIT Delhi Delhi India IIT Jodhpur Jodhpur Rajasthan India IBM TJ Watson Res Ctr Yorktown Hts NY USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Several successful adversarial attacks have demonstrated the vulnerabilities of deep learning algorithms. These attacks are detrimental in building deep learning based dependable AI applications. Therefore, it is imperative to build a defense mechanism to protect the integrity of deep learning models. In this paper, we present a novel "defense layer" in a network which aims to block the generation of adversarial noise and prevents an adversarial attack in black-box and gray-box settings. The parameter-free defense layer, when applied to any convolutional network, helps in achieving protection against attacks such as FGSM, L2, Elastic-Net, and DeepFool. Experiments are performed with different CNN architectures, including VGG, ResNet, and DenseNet, on three databases, namely, MNIST, CIFAR-10, and PaSC. The results showcase the efficacy of the proposed defense layer without adding any computational overhead. For example, on the CIFAR-10 database, while the attack can reduce the accuracy of the ResNet-50 model to as low as 6.3%, the proposed "defense layer" retains the original accuracy of 81.32%.

关键词： Mathematical model Perturbation methods Machine learning computer architecture Robustness Computational modeling Databases

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Discriminant Distribution-Agnostic Loss for Facial Expression recognition in the Wild

Discriminant Distribution-Agnostic Loss for Facial Expressio...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Farzaneh, Amir Hossein Qi, Xiaojun Utah State Univ Dept Comp Sci Logan UT 84322 USA

ISBN: (纸本)9781728193601

Facial Expression recognition (FER) has demonstrated remarkable progress due to the advancement of deep Convolutional Neural Networks (CNNs). FER's goal as a visual recognition problem is to learn a mapping from the facial embedding space to a set of fixed expression categories using a supervised learning algorithm. Softmax loss as the de facto standard in practice fails to learn discriminative features for efficient learning. Center loss and its variants as promising solutions increase deep feature discriminability in the embedding space and enable efficient learning. They fundamentally aim to maximize intra-class similarity and inter-class separation in the embedding space. However, center loss and its variants ignore the underlying extreme class imbalance in challenging wild FER datasets. As a result, they lead to a separation bias toward majority classes and leave minority classes overlapped in the embedding space. In this paper, we propose a novel Discriminant Distribution-Agnostic loss (DDA loss) to optimize the embedding space for extreme class imbalance scenarios. Specifically, DDA loss enforces inter-class separation of deep features for both majority and minority classes. Any CNN model can be trained with the DDA loss to yield well separated deep feature clusters in the embedding space. We conduct experiments on two popular large-scale wild FER datasets (RAF-DB and AffectNet) to show the discriminative power of the proposed loss function.

关键词： Face recognition

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An Interface between Grassmann manifolds and vector spaces

An Interface between Grassmann manifolds and vector spaces

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Souza, Lincon S. Sogi, Naoya Gatto, Bernardo B. Kobayashi, Takumi Fukui, Kazuhiro Univ Tsukuba Grad Sch Sci & Technol Tsukuba Ibaraki Japan Univ Tsukuba Ctr Artificial Intelligence Res C AIR Tsukuba Ibaraki Japan Natl Inst Adv Ind Sci & Technol Tsukuba Ibaraki Japan

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

In this paper, we propose a method to map data from a Grassmann manifold to a vector space while maximizing discrimination capability for classification. Subspaces are a practical and robust representation for image set recognition. However, as they exist on a Grassmann manifold, machine learning tools constructed on Euclidean geometry cannot be promptly utilized. Recently, methods to construct end-to-end learnable models for subspaces are starting to be explored, but they require multiple matrix decompositions and can be hard to compute and extend. Therefore we introduce a layer to map Grassmann manifold-valued data to vector space, in such a way that it can be seamlessly used as a layer along with other powerful tools defined on Euclidean space. The key idea of our method is to formulate the manifold logarithmic map (log) as a learnable model, where we seek to learn a tangency point that minimizes a loss function with respect to the data. The log effectively transforms a manifold point into a tangent vector. This log model can be learned with Riemannian stochastic gradient descent on the target manifold. We demonstrate the effectiveness of our proposed method on the applications of hand shape recognition, face identification and facial emotion recognition.

关键词： Manifolds Image recognition Tools Computational modeling Kernel Machine learning Data models

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Proceedings - 2020 ieee/CVF conference on computer vision and pattern recognition, cvpr 2020

Proceedings - 2020 IEEE/CVF Conference on Computer Vision an...

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2020 ieee/CVF conference on computer vision and pattern recognition, cvpr 2020

The proceedings contain 2 papers. The topics discussed include: attention mechanism exploits temporal contexts: real-time 3D human pose reconstruction;and cascaded deep monocular 3D human pose estimation with evolutio...

ISBN: (纸本)9781728171685

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Fine grained pointing recognition for natural drone guidance

Fine grained pointing recognition for natural drone guidance

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Barbed, O. L. Azagra, P. Teixeira, L. Chli, M. Civera, J. Murillo, A. C. Univ Zaragoza DIIS I3A Zaragoza Spain Swiss Fed Inst Technol Vis Robot Lab Zurich Switzerland

ISBN: (纸本)9781728193601

Human action recognition systems are typically focused on identifying different actions, rather than fine grained variations of the same action. This work explores strategies to identify different pointing directions in order to build a natural interaction system to guide autonomous systems such as drones. Commanding a drone with hand-held panels or tablets is common practice but intuitive user-drone interfaces might have significant benefits. The system proposed in this work just requires the user to provide occasional high-level navigation commands by pointing the drone towards the desired motion direction. Due to the lack of data on these settings, we present a new benchmarking video dataset to validate our framework and facilitate future research on the area. Our results show good accuracy for pointing direction recognition, while running at interactive rates and exhibiting robustness to variability in user appearance, viewpoint, camera distance and scenery.

关键词： Drones

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AdaMT-Net: An Adaptive Weight Learning Based Multi-Task Learning Model For Scene Understanding

AdaMT-Net: An Adaptive Weight Learning Based Multi-Task Lear...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Jha, Ankit Kumar, Awanish Banerjee, Biplab Chaudhuri, Subhasis Indian Inst Technol Dept Elect Engn Mumbai Maharashtra India Indian Inst Technol Ctr Studies Resources Engn Mumbai Maharashtra India

ISBN: (纸本)9781728193601

We tackle the problem of deep end-to-end multi-task learning (MTL) for visual scene understanding from monocular images in this paper. It is proven that learning several related tasks together helps in attaining improved performance per-task than training them independently. This is due to the fact that related tasks share important feature characteristics among themselves, which the MTL techniques can effectively explore for improved joint training. Based on this premise, we are interested in generic to specific feature extraction given the different tasks within a common framework. To this end, we propose a typical U-Net based encoder-decoder architecture called AdaMT-Net, where the densely-connected deep convolutional neural network (CNN) based feature encoder is shared among the tasks while the soft-attention based task-specific decoder modules produce the desired outcomes. One major issue in MTL is to assign the weights for the task-specific loss-terms in the final cumulative optimization function. As opposed to the manual approach, we propose a novel adaptive weight learning strategy by carefully exploring the loss-gradients per-task over the training iterations. Experimental results on the benchmark CityScapes, NYUv2, and ISPRS datasets confirm that AdaMT-Net achieves state-of-the-art performance on most of the evaluation metrics.

关键词： computer vision

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