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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

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Deep Residual Network with Enhanced Upscaling Module for Super-Resolution 31

Deep Residual Network with Enhanced Upscaling Module for Sup...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Kim, Jun-Hyuk Lee, Jong-Seok Yonsei Univ Sch Integrated Technol Seoul South Korea

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Single image super-resolution (SR) have recently shown great performance thanks to the advances in deep learning. In the middle of the deep networks for SR, a part that increases image resolution is required, for which a sub-pixel convolution layer is known as an efficient way. However, we argue that the method has room for improvement, and propose an enhanced upscaling module (EUM), which achieves improvement by utilizing nonlinear operations and skip connections. Employing our proposed EUM, we propose a novel deep residual network for SR, called EUSR. Our proposed EUSR was ranked in the 9th place among 24 teams in terms of SSIM in track 1 of the NTIRE 2018 SR Challenge [25]. In addition, we experimentally show that EUSR has comparable performance on x2 and x4 SR for four benchmark datasets to the state-of-the-art methods, and outperforms them on a large scaling factor (x8).

关键词： Convolution Image resolution Feature extraction Training Image reconstruction computer vision Signal resolution

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IR2VI: Enhanced Night Environmental Perception by Unsupervised Thermal Image Translation 31

IR2VI: Enhanced Night Environmental Perception by Unsupervis...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Liu, Shuo John, Vijay Blasch, Erik Liu, Zheng Huang, Ying Univ British Columbia Vancouver BC Canada Toyota Inst Technol Nagoya Aichi Japan Air Force Res Lab Wright Patterson AFB OH USA Chongqing Univ Posts & Telecommun Chongqing Peoples R China

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Context enhancement is critical for night vision (NV) applications, especially for the dark night situation without any artificial lights. In this paper, we present the infrared-to-visual (IR2VI) algorithm, a novel unsupervised thermal-to-visible image translation framework based on generative adversarial networks (GANs). IR2VI is able to learn the intrinsic characteristics from VI images and integrate them into IR images. Since the existing unsupervised GAN-based image translation approaches face several challenges, such as incorrect mapping and lack of fine details, we propose a structure connection module and a region-of-interest (ROI) focal loss method to address the current limitations. Experimental results show the superiority of the IR2VI algorithm over baseline methods.

关键词： Training Gallium nitride Image fusion Generators Night vision Task analysis Semantics

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Autonomous detection of disruptions in the intensive care unit using deep mask R-CNN 31

Autonomous detection of disruptions in the intensive care un...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Malhotra, Kumar Rohit Davoudi, Anis Siegel, Scott Bihorac, Azra Rashidi, Parisa Univ Florida Gainesville FL 32611 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Patients staying in the Intensive Care Unit (ICU) have a severely disrupted circadian rhythm. Due to patients' critical medical condition, ICU physicians and nurses have to provide round-the-clock clinical care, further disrupting patients' circadian rhythm. Mistimed family visits during rest-time can also disrupt patients' circadian rhythm. Currently, such effects are only reported based on hospital visitation policies rather than the actual number of visitors and care providers in the room. To quantify visitation disruptions, we used a deep Mask R-CNN model, a deep learning framework for object instance segmentation to detect and quantify the number of individuals in the ICU unit. This study represents the first effort to automatically quantify visitations in an ICU room, which could have implications in terms of policy adjustment, as well as circadian rhythm investigation. Our model achieved precision of 0.97 and recall of 0.67, with F1 score of 0.79 for detecting disruptions in the ICU units.

关键词： Hospitals Circadian rhythm Biomedical imaging computer vision Timing Lighting conferences

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Understanding Deep Neural Networks For Regression In Leaf Counting

Understanding Deep Neural Networks For Regression In Leaf Co...

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： Andrei Dobrescu Mario Valerio Giuffrida Sotirios A. Tsaftaris University of Edinburgh University of Edinburgh The Alan Turing Institute

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Deep learning methods are constantly increasing in popularity and success across a wide range of computer vision applications. However, they are perceived as 'black boxes', due to the lack of an intuitive interpretation of their decision processes. We present a study aimed at understanding how Deep Neural Networks (DNN) reach a decision in regression tasks. This study focuses on deep learning approaches in the common plant phenotyping task of leaf counting. We employ Layer-wise Relevance Propagation (LRP) and Guided Back Propagation to provide insight into which parts of the input contribute to intermediate layers and the output. We observe that the network largely disregards the background and focuses on the plant during training. More importantly, we found that the leaf blade edges are the most relevant part of the plant for the network model in the counting task. Results are evaluated using a VGG-16 deep neural network on the CVPPP 2017 Leaf Counting Challenge dataset.

关键词： Regression (Psychology) Neural network network model BLACK-BOX plants foliage Comprehension images computer vision Popularity

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Detection of Distracted Driver using Convolutional Neural Network 31

Detection of Distracted Driver using Convolutional Neural Ne...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Baheti, Bhakti Gajre, Suhas Talbar, Sanjay SGGS Inst Engn & Technol Ctr Excellence Signal & Image Proc Nanded Maharashtra India

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Number of road accidents is continuously increasing in last few years worldwide. As per the survey of National Highway Traffic Safety Administrator, nearly one in five motor vehicle crashes are caused by distracted driver. We attempt to develop an accurate and robust system for detecting distracted driver and warn him against it. Motivated by the performance of Convolutional Neural Networks in computer vision, we present a CNN based system that not only detects the distracted driver but also identifies the cause of distraction. VGG-16 architecture is modified for this particular task and various regularization techniques are implied in order to improve the performance. Experimental results show that our system outperforms earlier methods in literature achieving an accuracy of 96.31% and processes 42 images per second on GPU. We also study the effect of dropout, L2 regularization and batch normalisation on the performance of the system. Next, we present a modified version of our architecture that achieves 95.54% classification accuracy with the number of parameters reduced from 140M in original VGG-16 to 15M only.

关键词： Vehicles computer architecture Vehicle crash testing Convolutional neural networks Task analysis Wheels Roads

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A Digital Image Processing Pipeline for Modelling of Realistic Noise in Synthetic Images

A Digital Image Processing Pipeline for Modelling of Realist...

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ieee/cvf conference on computer vision and pattern recognition workshops

作者： Oleksandra Bielova Ronny Hansch Andreas Ley Olaf Hellwich Technische Universitat Berlin

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

The evaluation of computer vision methods on synthetic images offers control over scene, object, and camera properties. The disadvantage is that synthetic data usually lack many of the effects of real cameras that pose the actual challenge to the methods under investigation. Among those, noise is one of the effects more difficult to simulate as it changes the signal at an early stage and is strongly influenced by the camera's internal processing chain. The resulting noise is highly complex, intensity dependent, as well as spatially and spectrally correlated. We propose to transform synthetic images into the raw format of digital cameras, alter them with a physically motivated noise model, and then apply a processing chain that resembles a digital camera. Experiments show that the resulting noise exhibits a strong similarity to noise in real digital images, which further decreases the gap between synthesized images and real photographs.

关键词： Noise images Digital cameras COMPOSITE IMAGES Modeling Digital images Cameras Noise models Image processing SYNTHETICS computer vision pipeline Real number

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Learning Fashion By Simulated Human Supervision 31

Learning Fashion By Simulated Human Supervision

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Alshan, Eli Alpert, Sharon Neuberger, Assaf Bubis, Nathaniel Oks, Eduard Amazon Lab126 Sunnyvale CA 94089 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

We consider the task of predicting subjective fashion traits from images using neural networks. Specifically, we are interested in training a network for ranking outfits according to how well they fit the user. In order to capture the variability induced by human subjective considerations, each training example is annotated by a panel of fashion experts. Similarly to previous works on subjective data, the panel votes are converted to a classification or regression problem and the corresponding network is trained and evaluated using standard objective metrics. The question is which objective metric, if any, is most suitable to measure the performance of a network trained for subjective tasks? In this paper, we conducted human approval tests for outfit ranking networks trained using various objective metrics. We show that these metrics do not adequately estimate the human approval of subjective tasks. Instead, we introduce a supervising network that unlike objective metrics, is designed to capture the variability induced by human subjectivity. We use it to supervise our outfit ranking network and we demonstrate empirically, that training our outfit ranking network with the suggested supervising network achieves greater approval ratings from human subjects.

关键词： Task analysis Training Measurement Level set Prediction algorithms computer vision Standards

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Learning Network Architectures of Deep CNNs under Resource Constraints 31

Learning Network Architectures of Deep CNNs under Resource C...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Chan, Michael Scarafoni, Daniel Duarte, Ronald Thornton, Jason Skelly, Luke MIT Lincoln Lab 244 Wood St Lexington MA 02421 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Recent works in deep learning have been driven broadly by the desire to attain high accuracy on certain challenge problems. The network architecture and other hyper-parameters of many published models are typically chosen by trial-and-error experiments with little considerations paid to resource constraints at deployment time. We propose a fully automated model learning approach that (1) treats architecture selection as part of the learning process, (2) uses a blend of broad-based random sampling and adaptive iterative refinement to explore the solution space, (3) performs optimization subject to given memory and computational constraints imposed by target deployment scenarios, and (4) is scalable and can use only a practically small number of GPUs for training. We present results that show graceful model degradation under strict resource constraints for object classification problems using CIFAR-10 in our experiments. We also discuss future work in further extending the approach.

关键词： computer architecture Computational modeling Optimization Adaptation models Network architecture Linear programming Training

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Dual Graphical Models for Relational Modeling of Indoor Object Categories

Dual Graphical Models for Relational Modeling of Indoor Obje...

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： Lin Guo Guoliang Fan Weihua Sheng School of Electrical and Computer Engineering Oklahoma State University

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

There are three levels for indoor scene understanding, pixel level labeling, object level recognition and scene level holistic understanding. The three levels provide complementary bottom-up scene representation. Traditional research often addresses these three tasks separately where the three levels of semantic data are seldom jointly considered. We propose a new method to bridge the three semantic levels by using dual graphical models for relational modeling of object categories in indoor scenes. The vertical placement model captures top-down object configuration by which the visible pixels of some accessory objects could be used to infer the presence of a supportive object underneath. The horizontal placement model reveals how multiple object categories are related to each other on the ground in different indoor scenes. The experimental results show improvements on the bounding box accuracy using both vertical and horizontal placement models from pixel level labeling.

关键词： Graphical models pixel three-level vertical placement target configurations Dual INDOORS

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KCNN: Extremely-Efficient Hardware Keypoint Detection with a Compact Convolutional Neural Network 31

KCNN: Extremely-Efficient Hardware Keypoint Detection with a...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Di Febbo, Paolo Dal Mutto, Carlo Tieu, Kinh Mattoccia, Stefano Aquifi Inc Palo Alto CA 94303 USA Univ Bologna Bologna Italy

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Keypoint detection algorithms are typically based on handcrafted combinations of derivative operations implemented with standard image filtering approaches. The early layers of Convolutional Neural Networks (CNNs) for image classification, whose implementation is nowadays often available within optimized hardware units, are characterized by a similar architecture. Therefore, the exploration of CNNs for keypoint detection is a promising avenue to obtain a low-latency implementation, also enabling to effectively move the computational cost of the detection to dedicated Neural Network processing units. This paper proposes a methodology for effective keypoint detection by means of an efficient CNN characterized by a compact three-layer architecture. A novel training procedure is proposed for learning values of the network parameters which allow for an approximation of the response of handcrafted detectors, showing that the proposed architecture is able to obtain results comparable with the state of the art. The capability of emulating different detectors allows to deploy a variety of algorithms to dedicated hardware by simply retraining the network. A sensor-based FPGA implementation of the introduced CNN architecture is presented, allowing latency smaller than 1[ms].

关键词： Detectors Training computer architecture Hardware Field programmable gate arrays Convolution Complexity theory

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