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DeepRing: Protecting Deep Neural Network with Blockchain 32

DeepRing: Protecting Deep Neural Network with Blockchain

引用

32nd IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Goel, Akhil Agarwal, Akshay Vatsa, Mayank Singh, Richa Ratha, Nalini IIIT Delhi Delhi NY 13753 USA IBM Res Ossining NY USA

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125060

Several computer vision applications such as object detection and face recognition have started to completely rely on deep learning based architectures. These architectures, when paired with appropriate loss functions and optimizers, produce state-of-the-art results in a myriad of problems. On the other hand, with the advent of "blockchain", the cybersecurity industry has developed a new sense of trust which was earlier missing from both the technical and commercial perspectives. Employment of cryptographic hash, as well as symmetric/asymmetric encryption and decryption algorithms, ensure security without any human intervention (i.e., centralized authority). In this research, we present the synergy between the best of both these worlds. We first propose a model which uses the learned parameters of a typical deep neural network and is secured from external adversaries by cryptography and blockchain technology. As the second contribution of the proposed research, a new parameter tampering attack is proposed to properly justify the role of blockchain in machine learning.

关键词： Public key computer architecture Machine learning

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Scene Representation in Bird's-Eye View from Surrounding Cameras with Transformers

Scene Representation in Bird's-Eye View from Surrounding Cam...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhao, Yun Zhang, Yu Gong, Zhan Zhu, Hong Inspur Elect Informat Ind Co Ltd Dept AI & HPC Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Scene representation in the bird's-eye-view (BEV) coordinate frame provides a succinct and effective way to understand surrounding environments for autonomous vehicles and robotics. In this work, we present an end-to-end architecture to generate the BEV representation from surrounding cameras. To generate the BEV representation, we propose a transformer-based encoder-decoder structure to translate the image features from different cameras into the BEV frame, which takes advantage of the context information in the individual image and the relationship between images in different views. We perform multiple semantic segmentation tasks using the BEV features. Experimental results show that our model outperforms the competitive baseline [20], which demonstrates the effectiveness and efficiency of our method.

关键词： Image segmentation Image coding Computational modeling Semantics Robot vision systems Predictive models Cameras

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X-ray Testing by computer vision

X-ray Testing by Computer Vision

引用

26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Mery, Domingo Pontificia Univ Catolica Chile Dept Comp Sci Santiago Chile

ISBN: (纸本)9780769549903

X-ray imaging has been developed not only for its use in medical imaging for human beings, but also for materials or objects, where the aim is to analyze -nondestructively-those inner parts that are undetectable to the naked eye. Thus, X-ray testing is used to determine if a test object deviates from a given set of specifications. Typical applications are analysis of food products, screening of baggage, inspection of automotive parts, and quality control of welds. In order to achieve efficient and effective X-ray testing, automated and semi-automated systems are being developed to execute this task. In this paper, we present a general overview of computer vision methodologies that have been used in X-ray testing. In addition, we review some techniques that have been applied in certain relevant applications;and we introduce a public database of X-ray images that can be used for testing and evaluation of image analysis and computer vision algorithms. Finally, we conclude that the following: that there are some areas -like casting inspection-where automated systems are very effective, and other application areas -such as baggage screening-where human inspection is still used;there are certain application areas -like weld and cargo inspections-where the process is semi-automatic;and there is some research in areas including food analysis-where processes are beginning to be characterized by the use of X-ray imaging.

关键词： X-ray imaging casting computer vision computerised instrumentation inspection

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Finding your Lookalike: Measuring Face Similarity Rather than Face Identity 31

Finding your Lookalike: Measuring Face Similarity Rather tha...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Sadovnik, Amir Gharbi, Wassim Thanh Vu Gallagher, Andrew Lafayette Coll Easton PA 18042 USA Google Res Mountain View CA USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Face images are one of the main areas of focus for computer vision, receiving on a wide variety of tasks. Although face recognition is probably the most widely researched, many other tasks such as kinship detection, facial expression classification and facial aging have been examined. In this work we propose the new, subjective task of quantifying perceived face similarity between a pair of faces. That is, we predict the perceived similarity between facial images, given that they are not of the same person. Although this task is clearly correlated with face recognition, it is different and therefore justifies a separate investigation. Humans often remark that two persons look alike, even in cases where the persons are not actually confused with one another. In addition, because face similarity is different than traditional image similarity, there are challenges in data collection and labeling, and dealing with diverging subjective opinions between human labelers. We present evidence that finding facial look-alikes and recognizing faces are two distinct tasks. We propose a new dataset for facial similarity and introduce the Lookalike network, directed towards similar face classification, which outperforms the ad hoc usage of a face recognition network directed at the same task.

关键词： Face Face recognition Task analysis Training Feature extraction computer vision Neural networks

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Generative Zero-shot Network Quantization

Generative Zero-shot Network Quantization

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： He, Xiangyu Lu, Jiahao Xu, Weixiang Hu, Qinghao Wang, Peisong Cheng, Jian Chinese Acad Sci Inst Automat Beijing Peoples R China

ISBN: (纸本)9781665448994

Convolutional neural networks are able to learn realistic image priors from numerous training samples in low-level image generation and restoration [66]. We show that, for high-level image recognition tasks, we can further reconstruct "realistic" images of each category by leveraging intrinsic Batch Normalization (BN) statistics without any training data. Inspired by the popular VAE/GAN methods, we regard the zero-shot optimization process of synthetic images as generative modeling to match the distribution of BN statistics. The generated images serve as a calibration set for the following zero-shot network quantizations. Our method meets the needs for quantizing models based on sensitive information, e.g., due to privacy concerns, no data is available. Extensive experiments on benchmark datasets show that, with the help of generated data, our approach consistently outperforms existing data-free quantization methods.

关键词： Training Quantization (signal) Image resolution Image synthesis Training data pattern recognition Image restoration

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Deep Video Codec Control for vision Models

Deep Video Codec Control for Vision Models

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Reich, Christoph Debnath, Biplob Patel, Deep Prangemeier, Tim Cremers, Daniel Chakradhar, Srimat NEC Labs Amer Inc San Jose CA 95110 USA Tech Univ Munich Munich Germany Tech Univ Darmstadt Darmstadt Germany Munich Ctr Machine Learning MCML Munich Germany

ISBN: (纸本)9798350365474

Standardized lossy video coding is at the core of almost all real-world video processing pipelines. Rate control is used to enable standard codecs to adapt to different network bandwidth conditions or storage constraints. However, standard video codecs (e.g., H.264) and their rate control modules aim to minimize video distortion w.r.t. human quality assessment. We demonstrate empirically that standard-coded videos vastly deteriorate the performance of deep vision models. To overcome the deterioration of vision performance, this paper presents the first end-to-end learnable deep video codec control that considers both bandwidth constraints and downstream deep vision performance, while adhering to existing standardization. We demonstrate that our approach better preserves downstream deep vision performance than traditional standard video coding.

关键词： Codec Coding for Machines Deep Video Codec Control Deep vision Models End-to-end Learning Rate Control Self-supervised Learning Standardization Video Coding

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Anticipation of Human Actions with Pose-based Fine-grained Representations 32

Anticipation of Human Actions with Pose-based Fine-grained R...

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32nd IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Agethen, Sebastian Lee, Hu-Cheng Hsu, Winston H. Natl Taiwan Univ Taipei Taiwan

ISBN: (纸本)9781728125060

Anticipating an action that is about to happen allows us to be more efficient in interacting with our environment. However, prediction is a challenging task in computer vision, because videos are only partially available when a decision is to be made. Complicating the issue is that it is not always clear which of the visible activities in the scene are relevant to the action, and which ones are not. We suggest that the key to recognizing an action lies with the human actors, and that it is therefore necessary for the prediction process to attend to persons in a scene. In our work, we extract fine-grained features on visible human actors and predict the future via an L2-regression in feature space. This allows the regressed future feature to focus on the actor. Using this, the future action is classified. More specifically, the fine-grained extraction is guided by a pose prediction system that models current and future human poses in the scene. We run qualitative and quantitative experiments on the Charades dataset, and initial results show that our system improves action prediction.

关键词： computer vision

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A Smart Sensor with Hyperspectral/Range Fovea and Panoramic Peripheral View

A Smart Sensor with Hyperspectral/Range Fovea and Panoramic ...

引用

IEEE-computer-Society conference on computer vision and pattern recognition workshops

作者： Wang, Tao Zhu, Zhigang Rhody, Harvey CUNY City Coll Dept Comp Sci 138th St & Convent Ave New York NY 10031 USA CUNY Dept Comp Sci New York NY 10016 USA Rochester Inst Technol Ctr Imaging Sci Rochester NY 14623 USA

ISBN: (纸本)9781424439942

We propose an adaptive and effective multimodal peripheral-fovea sensor design for real-time targets tracking. This design is inspired by the biological vision systems for achieving real-time target detection and recognition with a hyperspectral/range fovea and panoramic peripheral view. A realistic scene simulation approach is used to evaluate our sensor design and the related data exploitation algorithms before a real sensor is made. The goal is to reduce development time and system cost while achieving optimal results through an iterative process that incorporates simulation, sensing, processing and evaluation. Important issues such as multimodal sensory component integration, region of interest extraction, target tracking, hyperspectral image analysis and target signature identification are discussed.

关键词： Hyperspectral imaging panoramic vision bio-inspired sensing target tracking

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vision based autonomous orientational control for aerial manipulation via on-board FPGA 29

Vision based autonomous orientational control for aerial man...

引用

29th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Suphachart, Leewiwatwong Shimahara, Syohei Ladig, Robert Shimonomura, Kazuhiro Ritsumeikan Univ Dept Robot Kusatsu Shiga 5258577 Japan

ISBN: (纸本)9781509014378

We describe an FPGA-based on-board control system for autonomous orientation of an aerial robot to assist aerial manipulation tasks. The system is able to apply yaw control to aid an operator to precisely position a drone when it is nearby a bar-like object. This is achieved by applying parallel Hough transform enhanced with a novel image space separation method, enabling highly reliable results in various circumstances combined with high performance. The feasibility of this approach is shown by applying the system to a multi-rotor aerial robot equipped with an upward directed robotic hand on top of the airframe developed for high altitude manipulation tasks. In order to grasp a bar-like object, orientation of the bar object is observed from the image data obtained by a monocular camera mounted on the robot. This data is then analyzed by the on-board FPGA system to control yaw angle of the aerial robot. In experiments, reliable yaw-orientation control of the aerial robot is achieved.

关键词： Field programmable gate arrays (FPGA)

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Deep Learning for Automatic Pneumonia Detection

Deep Learning for Automatic Pneumonia Detection

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Gabruseva, Tatiana Poplavskiy, Dmytro Kalinin, Alexandr Topcon Positioning Syst Brisbane Qld Australia Univ Michigan Ann Arbor MI 48109 USA Shenzhen Res Inst Big Data Shenzhen 518172 Guangdong Peoples R China

ISBN: (纸本)9781728193601

Pneumonia is the leading cause of death among young children and one of the top mortality causes worldwide. The pneumonia detection is usually performed through examine of chest X-Ray radiograph by highly-trained specialists. This process is tedious and often leads to a disagreement between radiologists. computer-aided diagnosis systems showed the potential for improving diagnostic accuracy. In this work, we develop the computational approach for pneumonia regions detection based on single-shot detectors, squeeze-and-extinction deep convolution neural networks, augmentations and multi-task learning. The proposed approach was evaluated in the context of the Radiological Society of North America Pneumonia Detection Challenge, achieving one of the best results in the challenge.

关键词： computer aided diagnosis

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