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共 8962 条记录，以下是381-390 订阅

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Improving In-field Cassava Whitefly Pest Surveillance with Machine Learning

Improving In-field Cassava Whitefly Pest Surveillance with M...

引用

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

作者： Tusubira, Jeremy Francis Nsumba, Solomon Ninsiima, Flavia Akera, Benjamin Acellam, Guy Nakatumba, Joyce Mwebaze, Ernest Quinn, John Oyana, Tonny Makerere Univ Artificial Intelligence Lab Kampala Uganda Google Res Mountain View CA USA Makerere Univ Geospatial Data & Computat Intelligence Lab Kampala Uganda

ISBN: (纸本)9781728193601

Whiteflies are the major vector responsible for the transmission of cassava related diseases in tropical environments, and knowing the numbers of whiteflies is key in detecting and identifying their spread and prevention. However, the current approach for counting whiteflies is a simple visual inspection, where a cassava leaf is turned upside down to reveal the underside where the whiteflies reside to enable a manual count. Repeated across many cassava farms, this task is quite tedious and time-consuming. In this paper, we propose a method to automatically count whiteflies using computer vision techniques. To implement this approach, we collected images of infested cassava leaves and trained a computer vision detector using Haar Cascade and Deep Learning techniques. The two techniques were used to identify the pest in images and return a count. Our results show that this novel method produces a white-fly count with high precision. This method could be applied to similar object detection scenarios similar to the whitefly problem with minor adjustments.

关键词： computer vision

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CoRe: Color Regression for Multicolor Fashion Garments

CoRe: Color Regression for Multicolor Fashion Garments

引用

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

作者： Rame, Alexandre Douillard, Arthur Ollion, Charles Sorbonne Univ Paris France Heuritech Paris France

ISBN: (纸本)9781665487399

Developing deep networks that analyze fashion garments has many real-world applications. Among all fashion attributes, color is one of the most important yet challenging to detect. Existing approaches are classification-based and thus cannot go beyond the list of discrete predefined color names. In this paper, we handle color detection as a regression problem to predict the exact RGB values. That's why in addition to a first color classifier, we include a second regression stage for refinement in our newly proposed architecture. This second step combines two attention models: the first depends on the type of clothing, the second depends on the color previously detected by the classifier. Our final prediction is the weighted spatial pooling over the image pixels RGB values, where the illumination has been corrected. This architecture is modular and easily expanded to detect the RGBs of all colors in a multicolor garment. In our experiments, we show the benefits of each component of our architecture.

关键词： Visualization computer vision Image color analysis conferences Clothing Lighting computer architecture

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Unpaired Faces to Cartoons: Improving XGAN

Unpaired Faces to Cartoons: Improving XGAN

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

作者： Ramos, Stev H. Cabrera, Joel Ibanez, Daniel Jimenez-Panta, Alejandro B. Beltran-Castanon, Cesar Villanueva, Edwin Pontifical Catholic Univ Peru Lima Peru

ISBN: (纸本)9781665487399

Domain Adaptation is a task that aims to translate an image from a source domain to a desired target domain. Current methods in domain adaptation use adversarial training based on Generative Adversarial Networks (GAN). In the present work, we focus on the task of domain adaptation from real faces to cartoon face images. We start from a baseline architecture called XGAN and introduce some improvements to it. Our proposed model is called W-XDGAN, which uses a form of GAN called Wasserstein-GAN, learns to approximate the Wasserstein Distance, and adds a denoiser to smooth the output cartoons. Whereas the original XGAN paper only presented a qualitative analysis, the advantages of this solution are demonstrated both quantitatively and qualitatively by comparing the results with models such as UNIT and original XGAN. Our code and models are publicly available at https://github. com/IAmigos/avatar-image-generator.

关键词： Training computer vision Codes Computational modeling conferences computer architecture Generative adversarial networks

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Image Reconstruction from Neuromorphic Event Cameras using Laplacian-Prediction and Poisson Integration with Spiking and Artificial Neural Networks

Image Reconstruction from Neuromorphic Event Cameras using L...

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

作者： Duwek, Hadar Cohen Shalumov, Albert Tsur, Elishai Ezra Open Univ Israel Neurobiomorph Engn Lab NBEL Dept Math & Comp Sci Raanana Israel

ISBN: (纸本)9781665448994

Event cameras are robust neuromorphic visual sensors, which communicate transients in luminance as events. Current paradigm for image reconstruction from event data relies on direct optimization of artificial Convolutional Neural Networks (CNNs). Here we proposed a two-phase neural network, which comprises a CNN, optimized for Laplacian prediction followed by a Spiking Neural Network (SNN) optimized for Poisson integration. By introducing Laplacian prediction into the pipeline, we provide image reconstruction with a network comprising only 200 parameters. We converted the CNN to SNN, providing a full neuromorphic implementation. We further optimized the network with Mish activation and a novel convoluted CNN design, proposing a hybrid of spiking and artificial neural network with < 100 parameters. Models were evaluated on both N-MNIST and N-Caltech101 datasets.

关键词： Visualization Laplace equations Neuromorphics Pipelines Cameras Sensors pattern recognition

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When NAS Meets Trees: An Efficient Algorithm for Neural Architecture Search

When NAS Meets Trees: An Efficient Algorithm for Neural Arch...

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

作者： Qian, Guocheng Zhang, Xuanyang Li, Guohao Zhao, Chen Chen, Yukang Zhang, Xiangyu Ghanem, Bernard Sun, Jian King Abdullah Univ Sci & Technol KAUST Thuwal Saudi Arabia MEGVII Technol Beijing Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The key challenge in neural architecture search (NAS) is designing how to explore wisely in the huge search space. We propose a new NAS method called TNAS (NAS with trees), which improves search efficiency by exploring only a small number of architectures while also achieving a higher search accuracy. TNAS introduces an architecture tree and a binary operation tree, to factorize the search space and substantially reduce the exploration size. TNAS performs a modified bi-level Breadth-First Search in the proposed trees to discover a high-performance architecture. Impressively, TNAS finds the global optimal architecture on CIFAR-10 with test accuracy of 94.37% in four GPU hours in NAS-Bench-201. The average test accuracy is 94.35%, which outperforms the state-of-the-art. Code is available at: https://***/guochengqian/TNAS.

关键词： computer vision Costs Codes conferences Graphics processing units computer architecture pattern recognition

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Self-supervised vision Transformers for Land-cover Segmentation and Classification

Self-supervised Vision Transformers for Land-cover Segmentat...

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

作者： Scheibenreif, Linus Hanna, Joelle Mommert, Michael Borth, Damian Univ St Gallen AIML Lab Sch Comp Sci Rosenbergstr 30 St Gallen Switzerland

ISBN: (纸本)9781665487399

Transformer models have recently approached or even surpassed the performance of ConvNets on computer vision tasks like classification and segmentation. To a large degree, these successes have been enabled by the use of large-scale labelled image datasets for supervised pre-training. This poses a significant challenge for the adaption of vision Transformers to domains where datasets with millions of labelled samples are not available. In this work, we bridge the gap between ConvNets and Transformers for Earth observation by self-supervised pre-training on large-scale unlabelled remote sensing data(1). We show that self-supervised pre-training yields latent task-agnostic representations that can be utilized for both land cover classification and segmentation tasks, where they significantly outperform the fully supervised baselines. Additionally, we find that subsequent fine-tuning of Transformers for specific downstream tasks performs on-par with commonly used ConvNet architectures. An ablation study further illustrates that the labelled dataset size can be reduced to one-tenth after self-supervised pre-training while still maintaining the performance of the fully supervised approach.

关键词： Training Earth computer vision Image segmentation Computational modeling conferences Transformers

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On the Effect of Atmospheric Turbulence in the Feature Space of Deep Face recognition

On the Effect of Atmospheric Turbulence in the Feature Space...

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

作者： Robbins, Wes Boult, Terrance Univ Colorado Colorado Springs CO 80907 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

When captured over long distances, image quality is degraded by inconsistent refractive indexes in the atmosphere. This effect, known as Atmospheric Turbulence (AT), leads to lower performance for vision-based biometric systems such as face recognition. To account for AT, the literature has proposed methods to restore face-images from atmospheric turbulence, but has limited success. There is still a need to understand how atmospheric turbulence breaks recognition performance. We offer a first-look in this direction by providing a study on the effect of atmospheric turbulence in the feature space of deep-learning-based face recognition. We present results on recognition performance and feature space transformation caused by a wide range of AT levels. In deep feature space, we find interesting phenomena such as increasing feature magnitudes, which contradicts the expected result from the literature. From our results, we are able to identify an effect that makes face recognition under atmospheric turbulence uniquely difficult, which we call feature defection. In total, our findings suggest several areas of available improvement which can be used as a guideline for further progress in building models that are robust to AT.

关键词： Image quality computer vision Extraterrestrial phenomena Face recognition Atmospheric modeling conferences Computational modeling

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THETIS: THree Dimensional Tennis Shots A human action dataset

THETIS: THree Dimensional Tennis Shots A human action datase...

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

作者： Gourgari, Sofia Goudelis, Georgios Karpouzis, Konstantinos Kollias, Stefanos Natl Tech Univ Athens Image Video & Multimedia Syst Lab GR-10682 Athens Greece

ISBN: (纸本)9780769549903

The detection and classification of human movements, as a joint field of computer vision and pattern recognition, is used with an increasing rate in applications designed to describe human activity. Such applications require efficient methods and tools for the automatic analysis and classification of motion capture data, which constitute an active field of research. To facilitate the development and the benchmarking of methods for action recognition, several video collections have previously been proposed. In this paper, we present a new video database that can be used for an objective comparison and evaluation of different motion analysis and classification methods. The database contains video clips that capture the 3D motion of individuals. To be more specific, the set consists of 8374 video clips, which contain 12 different types of tennis actions performed by 55 individuals, captured by Kinect. Kinect provides the depth map of motion data and helps to extract the 3D skeletal joint connections. Performing experiments using state of the art algorithms, the database shows to be very challenging. It contains very similar to each other actions, offering the opportunity to algorithms dedicated to gaming and athletics, to be developed and tested. The database is freely available for research purposes.

关键词： computer vision feature extraction image classification image motion analysis interactive devices object detection object recognition sport video databases

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Ground Truth For Pedestrian Analysis and Application to Camera Calibration

Ground Truth For Pedestrian Analysis and Application to Came...

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

作者： Creusot, Clement Courty, Nicolas Toshiba R&D Ctr Kawasaki Kanagawa Japan UBS IRISA Vannes France

ISBN: (纸本)9780769549903

This paper investigates the use of synthetic 3D scenes to generate ground truth of pedestrian segmentation in 2D crowd video data. Manual segmentation of objects in videos is indeed one of the most time-consuming type of assisted labeling. A big gap in computer vision research can not be filled due to this lack of temporally dense and precise segmentation ground truth on large video samples. Such data is indeed essential to introduce machine learning techniques for automatic pedestrian segmentation, as well as many other applications involving occluded people. We present a new dataset of 1.8 million pedestrian silhouettes presenting human-to-human occlusion patterns likely to be seen in real crowd video data. To our knowledge, it is the first publicly available large dataset of pedestrian in crowd silhouettes. Solutions to generate and represent this data are detailed. We discuss ideas of how this ground truth can be used for a large number of computer vision applications and demonstrate it on a camera calibration toy problem.

关键词： calibration cameras computer vision image segmentation learning (artificial intelligence) video signal processing

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Contrastive Learning for Sports Video: Unsupervised Player Classification

Contrastive Learning for Sports Video: Unsupervised Player C...

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

作者： Koshkina, Maria Pidaparthy, Hemanth Elder, James H. York Univ Toronto ON Canada

ISBN: (纸本)9781665448994

We address the problem of unsupervised classification of players in a team sport according to their team affiliation, when jersey colours and design are not known a priori. We adopt a contrastive learning approach in which an embedding network learns to maximize the distance between representations of players on different teams relative to players on the same team, in a purely unsupervised fashion, without any labelled data. We evaluate the approach using a new hockey dataset and find that it outperforms prior unsupervised approaches by a substantial margin, particularly for real-time application when only a small number of frames are available for unsupervised learning before team assignments must be made. Remarkably, we show that our contrastive method achieves 94% accuracy after unsupervised training on only a single frame, with accuracy rising to 97% within 500 frames (17 seconds of game time). We further demonstrate how accurate team classification allows accurate team-conditional heat maps of player positioning to be computed.

关键词： Training Heating systems computer vision Image color analysis conferences Games Real-time systems

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