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Extensions and limitations of randomized smoothing for robustness guarantees

Extensions and limitations of randomized smoothing for robus...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Jamie Hayes University College London

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Randomized smoothing, a method to certify a classifier's decision on an input is invariant under adversarial noise, offers attractive advantages over other certification methods. It operates in a black-box and so certification is not constrained by the size of the classifier's architecture. Here, we extend the work of Li etal. [26], studying how the choice of divergence between smoothing measures affects the final robustness guarantee, and how the choice of smoothing measure itself can lead to guarantees in differing threat models. To this end, we develop a method to certify robustness against any ℓ p (p ∈ N >0 ) minimized adversarial perturbation. We then demonstrate a negative result, that randomized smoothing suffers from the curse of dimensionality; as p increases, the effective radius around an input one can certify vanishes.

关键词： Smoothing methods Robustness Perturbation methods computer architecture Random variables Visualization computer vision

来源：评论

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Continual Learning for Anomaly Detection in Surveillance Videos

Continual Learning for Anomaly Detection in Surveillance Vid...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Keval Doshi Yasin Yilmaz University of South Florida Tampa FL

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Anomaly detection in surveillance videos has been recently gaining attention. A challenging aspect of high-dimensional applications such as video surveillance is continual learning. While current state-of-the-art deep learning approaches perform well on existing public datasets, they fail to work in a continual learning framework due to computational and storage issues. Furthermore, online decision making is an important but mostly neglected factor in this domain. Motivated by these research gaps, we propose an online anomaly detection method for surveillance videos using transfer learning and continual learning, which in turn significantly reduces the training complexity and provides a mechanism for continually learning from recent data without suffering from catastrophic forgetting. Our proposed algorithm leverages the feature extraction power of neural network-based models for transfer learning, and the continual learning capability of statistical detection methods.

关键词： Videos Feature extraction Anomaly detection Training Neural networks Surveillance computer vision

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Extending Absolute Pose Regression to Multiple Scenes

Extending Absolute Pose Regression to Multiple Scenes

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Hunter Blanton Connor Greenwell Scott Workman Nathan Jacobs University of Kentucky DZYNE Technologies

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Direct pose regression using deep convolutional neural networks has become a highly active research area. However, even with significant improvements in performance in recent years, the best performance comes from training distinct, scene-specific networks. We propose a novel architecture, Multi-Scene PoseNet (MSPN), that allows for a single network to be used on an arbitrary number of scenes with only a small scene-specific component. Using our approach, we achieve competitive performance for two bench-mark 6DOF datasets, Microsoft 7Scenes and Cambridge Landmarks, while reducing the total number of network parameters significantly. Additionally, we demonstrate that our trained model serves as a better initialization for fine-tuning on new scenes compared to the standard ImageNet initialization, converging to lower error solutions within only a few epochs.

关键词： Cameras Training Feature extraction Databases Three-dimensional displays Standards Robot vision systems

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M2SGD: Learning to Learn Important Weights

M2SGD: Learning to Learn Important Weights

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Nicholas I-Hsien Kuo Mehrtash Harandi Nicolas Fourrier Christian Walder Gabriela Ferraro Hanna Suominen RSCS The Australian National University ECSE Monash University Australia Pole Universitaire Leonard de Vinci Paris La Defense France

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Meta-learning concerns rapid knowledge acquisition. One popular approach cast optimisation as a learning problem and it has been shown that learnt neural optimisers updated base learners more quickly than their handcrafted counterparts. In this paper, we learn an optimisation rule that sparsely updates the learner parameters and removes redundant weights. We present Masked Meta-SGD (M 2 SGD), a neural optimiser which is not only capable of updating learners quickly, but also capable of removing 83.71% weights for *** release our codes at https://***/Nic5472K/CLvision2020_CVPR_M2SGD.

关键词： Training Optimization Recurrent neural networks computer architecture Task analysis Image recognition Australia

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Learning Spatial Relationships between Samples of Patent Image Shapes

Learning Spatial Relationships between Samples of Patent Ima...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Juan Castorena Manish Bhattarai Diane Oyen Los Alamos National Laboratory Los Alamos NM USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Binary image based classification and retrieval of documents of an intellectual nature is a very challenging problem. Variations in the binary image generation mechanisms which are subject to the document artisan designer including drawing style, view-point, inclusion of multiple image components are plausible causes for increasing the complexity of the problem. In this work, we propose a method suitable to binary images which bridges some of the successes of deep learning (DL) to alleviate the problems introduced by the aforementioned variations. The method consists on extracting the shape of interest from the binary image and applying a non-Euclidean geometric neural-net architecture to learn the local and global spatial relationships of the shape. Empirical results show that our method is in some sense invariant to the image generation mechanism variations and achieves results outperforming existing methods in a patent image dataset benchmark.

关键词： Shape Three-dimensional displays Patents Feature extraction computer architecture Image segmentation Image generation

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Squeeze U-Net: A Memory and Energy Efficient Image Segmentation Network

Squeeze U-Net: A Memory and Energy Efficient Image Segmentat...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Nazanin Beheshti Lennart Johnsson Department of Computer Science University of Houston

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

To facilitate implementation of deep neural networks on embedded systems keeping memory and computation requirements low is critical, particularly for real-time mobile use. In this work, we propose a SqueezeNet inspired version of U-Net for image segmentation that achieves a 12X reduction in model size to 32MB, and 3.2X reduction in Multiplication Accumulation operations (MACs) from 287 billion ops to 88 billion ops for inference on the CamVid data set while preserving accuracy. Our proposed Squeeze U-Net is efficient in both low MACs and memory use. Our performance results using Tensorflow 1.14 with Python 3.6 and CUDA 10.1.243 on an NVIDIA K40 GPU shows that Squeeze U-Net is 17% faster for inference and 52% faster for training than U-Net for the same accuracy.

关键词： Fires Computational modeling Kernel Feature extraction Graphics processing units Memory management

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Color-wise Attention Network for Low-light Image Enhancement

Color-wise Attention Network for Low-light Image Enhancement

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yousef Atoum Mao Ye Liu Ren Ying Tai Xiaoming Liu Yarmouk University BOSCH Research North America Tencent YouTu Michigan State University

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Absence of nearby light sources while capturing an image will degrade the visibility and quality of the captured image, making computer vision tasks difficult. In this paper, a color-wise attention network (CWAN) is proposed for low-light image enhancement based on convolutional neural networks. Motivated by the human visual system when looking at dark images, CWAN learns an end-to-end mapping between low-light and enhanced images while searching for any useful color cues in the low-light image to aid in the color enhancement process. Once these regions are identified, CWAN attention will be mainly focused to synthesize these local regions, as well as the global image. Both quantitative and qualitative experiments on challenging datasets demonstrate the advantages of our method in comparison with state-of-the-art methods.

关键词： Image color analysis Noise reduction Task analysis Colored noise Lighting Frequency modulation computer vision

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Attentional Bottleneck: Towards an Interpretable Deep Driving Network

Attentional Bottleneck: Towards an Interpretable Deep Drivin...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Jinkyu Kim Mayank Bansal Waymo Research

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Deep neural networks are a key component of behavior prediction and motion generation for self-driving cars. One of their main drawbacks is a lack of transparency: they should provide easy to interpret rationales for what triggers certain behaviors. We propose an architecture called Attentional Bottleneck with the goal of improving transparency. Our key idea is to combine visual attention, which identifies what aspects of the input the model is using, with an information bottleneck that enables the model to only use aspects of the input which are important. This not only provides sparse and interpretable attention maps (e.g. focusing only on specific vehicles in the scene), but it adds this transparency at no cost to model accuracy. In fact, we find slight improvements in accuracy when applying Attentional Bottleneck to the ChauffeurNet model in comparison to a traditional visual attention model that degrades accuracy.

关键词： Visualization Heating systems Encoding Generators Neural networks computer architecture Roads

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Low-rate Image Compression with Super-resolution Learning

Low-rate Image Compression with Super-resolution Learning

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Wei Gao Lvfang Tao Linjie Zhou Dinghao Yang Xiaoyu Zhang Zixuan Guo School of Electronic and Computer Engineering Shenzhen Graduate School Peking University Shenzhen China Peng Cheng Laboratory Shenzhen China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

In this paper, we propose an end-to-end learned image compression framework for low-rate scenarios. Based on variational autoencoder, our method features a pair of compact-resolution and super-resolution networks, a set of hyper and main codec networks, and a conditional context model. The learning process of this framework is facilitated with integrated non-local attention modules and phase congruency priors. Multiple models are obtained from training with different hyper-parameters, and are jointly used in the image-level model selection process for rate control, which ensures that the bit rate constraint of the CLIC challenge is satisfied. Experimental results demonstrate that the proposed method can achieve an averaged multi-scale structural similarity (MS-SSIM) score of 0.9648 with bit rate consumption of 0.1499 bits per pixel, which outperforms the BPG image coding method significantly.

关键词： Image coding Image resolution Bit rate Training Codecs pattern recognition conferences

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Effective Deep-Learning-Based Depth Data Analysis on Low-Power Hardware for Supporting Elderly Care

Effective Deep-Learning-Based Depth Data Analysis on Low-Pow...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Christopher Pramerdorfer Rainer Planinc Martin Kampel Cogvis & TU Wien Vienna Austria Cogvis Vienna Austria TU Wien Vienna Austria

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

We present a detailed technical insight into a commercial vision-based sensor for monitoring residents in elderly care facilities and alerting caretakers in case of dangerous situations such as falls or residents not returning to their beds during nighttime. We focus on aspects that enable deeplearning-based object classification in realtime on low-end ARM-based hardware, which is prerequisite for a solution that is performant yet affordable, low-power, and unobtrusive. To this end, we introduce an efficient vision pipeline that maps the input depth data to concise virtual top-views. These views are then processed by a set of convolutional neural networks, with a scheduler selecting the most appropriate one based on the current operating conditions and available hardware resources. In order to overcome the challenge of acquiring large amounts of training data in this privacy-critical environment, we pretrain these networks on a large set of synthetic depth data. These concepts are general and applicable to similar vision tasks.

关键词： computer architecture Hardware Senior citizens Neural networks Motion detection Tracking Monitoring

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