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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"

共 3320 条记录，以下是2961-2970 订阅

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Reducing the feature divergence of RGB and near-infrared images using Switchable Normalization

Reducing the feature divergence of RGB and near-infrared ima...

引用

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

作者： Siwei Yang Shaozuo Yu Bingchen Zhao Yin Wang Tongji University Shanghai China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Visual pattern recognition over agricultural areas is an important application of aerial image processing. In this paper, we consider the multi-modality nature of agricultural aerial images and show that naively combining different modalities together without taking the feature divergence into account can lead to sub-optimal results. Thus, we apply a Switchable Normalization block to our DeepLabV3+ segmentation model to alleviate the feature divergence. Using the popular symmetric Kullback-Leibler divergence measure, we show that our model can greatly reduce the divergence between RGB and near-infrared channels. Together with a hybrid loss function, our model achieves nearly 10% improvements in mean IoU over previously published baseline.

关键词： Task analysis Computational modeling pattern recognition Switches computer vision Agriculture Semantics

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Intelligent Scene Caching to Improve Accuracy for Energy-Constrained Embedded vision

Intelligent Scene Caching to Improve Accuracy for Energy-Con...

引用

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

作者： Benjamin Simpson Ekdeep Lubana Yuchen Liu Robert Dick University of Michigan

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

We describe an efficient method of improving the performance of vision algorithms operating on video streams by reducing the amount of data captured and transferred from image sensors to analysis servers in a data-aware manner. The key concept is to combine guided, highly heterogeneous sampling with an intelligent Scene Cache. This enables the system to adapt to spatial and temporal patterns in the scene, thus reducing redundant data capture and processing. A software prototype of our framework running on a general-purpose embedded processor enables superior object detection accuracy (by 56%) at similar energy consumption (slight improvement of 4%) compared to an H.264 hardware accelerator.

关键词： Cameras Energy consumption Energy resolution Image resolution Streaming media Servers Object detection

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A Hardware Prototype Targeting Distributed Deep Learning for On-device Inference

A Hardware Prototype Targeting Distributed Deep Learning for...

引用

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

作者： Allen-Jasmin Farcas Guihong Li Kartikeya Bhardwaj Radu Marculescu The University of Texas at Austin Austin TX USA Arm Inc San Jose CA USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

This paper presents a hardware prototype and a framework for a new communication-aware model compression for distributed on-device inference. Our approach relies on Knowledge Distillation (KD) and achieves orders of magnitude compression ratios on a large pre-trained teacher model. The distributed hardware prototype consists of multiple student models deployed on Raspberry-Pi 3 nodes that run Wide ResNet and VGG models on the CIFAR10 dataset for real-time image classification. We observe significant reductions in memory footprint (50×), energy consumption (14×), latency (33×) and an increase in performance (12×) without any significant accuracy loss compared to the initial teacher model. This is an important step towards deploying deep learning models for IoT applications.

关键词： computer vision pattern recognition conferences

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SomethingFinder: Localizing undefined regions using referring expressions

SomethingFinder: Localizing undefined regions using referrin...

引用

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

作者： Sungmin Eum David Han Gordon Briggs US Army Research Lab US Naval Research Lab

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Previous research on localizing a target region in an image referred to by a natural language expression has occurred within an object-centric paradigm. However, in practice, there may not be any easily named or identifiable objects near a target location. Instead, references may need to rely on basic visual attributes, such as color or geometric clues. An expression like "a red something beside a blue vertical line" could still pinpoint a target location. As such, we begin to explore the open challenge of computational object-agnostic reference by constructing a novel dataset and by devising a new set of algorithms that can identify a target region in an image when given a referring expression containing only basic conceptual features.

关键词： Visualization Image color analysis Task analysis computer vision Image resolution Training Complexity theory

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VoronoiNet : General Functional Approximators with Local Support

VoronoiNet : General Functional Approximators with Local Sup...

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

作者： Francis Williams Jérôme Parent-Lévesque Derek Nowrouzezahrai Daniele Panozzo Kwang Moo Yi Andrea Tagliasacchi New York University McGill University University of Victoria Google Brain

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Voronoi diagrams are highly compact representations that are used in various Graphics applications. In this work, we show how to embed a differentiable version of it - via a novel deep architecture - into a generative deep network. By doing so, we achieve a highly compact latent embedding that is able to provide much more detailed reconstructions, both in 2D and 3D, for various shapes. In this tech report, we introduce our representation and present a set of preliminary results comparing it with recently proposed implicit occupancy networks.

关键词： Shape Three-dimensional displays Decoding Image reconstruction Training computer architecture Task analysis

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Leveraging combinatorial testing for safety-critical computer vision datasets

Leveraging combinatorial testing for safety-critical compute...

引用

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

作者： Christoph Gladisch Christian Heinzemann Martin Herrmann Matthias Woehrle Corporate Research Robert Bosch GmbH

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Deep learning-based approaches have gained popularity for environment perception tasks such as semantic segmentation and object detection from images. However, the different nature of a data-driven deep neural nets (DNN) to conventional software is a challenge for practical software verification. In this work, we show how existing methods from software engineering provide benefits for the development of a DNN and in particular for dataset design and analysis. We show how combinatorial testing based on a domain model can be leveraged for generating test sets providing coverage guarantees with respect to important environmental features and their interaction. Additionally, we show how our approach can be used for growing a dataset, i.e. to identify where data is missing and should be collected next. We evaluate our approach on an internal use case and two public datasets.

关键词： Testing Tools computer vision Computational modeling Analytical models Context modeling Software

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Story Completion with Explicit Modeling of Commonsense Knowledge

Story Completion with Explicit Modeling of Commonsense Knowl...

引用

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

作者： Mingda Zhang Keren Ye Rebecca Hwa Adriana Kovashka Department of Computer Science University of Pittsburgh

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Growing up with bedtime tales, even children could easily tell how a story should develop; but selecting a coherent and reasonable ending for a story is still not easy for machines. To successfully choose an ending requires not only detailed analysis of the context, but also applying commonsense reasoning and basic knowledge. Previous work [8] has shown that language models trained on very large corpora could capture common sense in an implicit and hard-to-interpret way. We explore another direction and present a novel method that explicitly incorporates commonsense knowledge from a structured dataset [11], and demonstrate the potential for improving story completion.

关键词： Tensile stress Predictive models Context modeling Knowledge based systems Cognition Training Task analysis

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A Deep Physical Model for Solar Irradiance Forecasting with Fisheye Images

A Deep Physical Model for Solar Irradiance Forecasting with ...

引用

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

作者： Vincent Le Guen Nicolas Thome EDF R&D France CEDRIC Conservatoire National des Arts et Métiers Paris France

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

We present a new deep learning approach for short-term solar irradiance forecasting based on fisheye images. Our architecture, based on recent works on video prediction with partial differential equations, extracts spatio-temporal features modelling cloud motion to accurately anticipate future solar irradiance. Our method obtains state-of-the-art results on video prediction and 5min-ahead irradiance forecasting against strong recent baselines, highlighting the benefits of incorporating physical knowledge in deep models for real-world physical process forecasting.

关键词： Forecasting Predictive models computer architecture Feature extraction Solar energy Machine learning Cameras

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Neurodata Lab’s approach to the Challenge on computer vision for Physiological Measurement

Neurodata Lab’s approach to the Challenge on Computer Visio...

引用

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

作者： Mikhail Artemyev Marina Churikova Mikhail Grinenko Olga Perepelkina Neurodata Lab LLC Miami USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

This paper introduces the Neurodata Lab's approach presented at the 1st Challenge on Remote Physiological Signal Sensing (RePSS) organized within CVPR2020. The RePSS challenge was focused on measuring the average heart rate from color facial videos, which is one of the most fundamental problems in the field of computer vision. Our deep learning-based approach includes 3D spatiotemporal attention convolutional neural network for photoplethysmogram extraction and 1D convolutional neural network pre-trained on synthetic data for time series analysis. It provides state-of-the-art results outperforming those of other participants on a mixture of VIPL and OBF databases: MAE=6.94 (12.3% improvement compared to the top-2 result), RMSE=10.68 (24.6% improvement), Pearson R = 0.755 (28.2% improvement).

关键词： Heart rate Three-dimensional displays Kernel Videos Estimation Face Convolution

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An a-contrario Biometric Fusion Approach

An a-contrario Biometric Fusion Approach

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

作者： Luis Di Martino Javier Preciozzi Rafael Grompone von Gioi Guillermo Garella Alicia Fernández Federico Lecumberry IIE Universidad de la República Uruguay Centre Borelli ENS Paris-Saclay Université Paris-Saclay France

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Fusion is a key component in many biometric systems: it is one of the most widely used techniques to improve their accuracy. Each time we need to combine the output of systems that use different biometric traits, or different samples of the same biometric trait, or even different algorithms, we need to define a fusion strategy. Independently of the fusion method used, there is always a decision step, in which it is decided if the traits being compared correspond to the same individual or not. In this work, we present a statistical decision criterion based on the a-contrario framework, which has already proven to be useful in biometric applications. The proposed method and its theoretical background is described in detail, and its application to biometric fusion is illustrated with simulated and real data.

关键词： Computational modeling Biological system modeling Stochastic processes pattern recognition Probability Estimation Training

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