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检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition Workshops"
23198 条 记 录,以下是211-220 订阅
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Line Art Colorization with Concatenated Spatial Attention
Line Art Colorization with Concatenated Spatial Attention
收藏 引用
ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Yuan, Mingcheng Simo-Serra, Edgar Waseda Univ Tokyo Japan
Line art plays a fundamental role in illustration and design, and allows for iteratively polishing designs. However, as they lack color, they can have issues in conveying final designs. In this work, we propose an int... 详细信息
来源: 评论
Neural Architecture Search of Deep Priors: Towards Continual Learning without Catastrophic Interference
Neural Architecture Search of Deep Priors: Towards Continual...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Mundt, Martin Pliushch, Iuliia Ramesh, Visvanathan Goethe Univ Frankfurt Germany
In this paper we analyze the classification performance of neural network structures without parametric inference. Making use of neural architecture search, we empirically demonstrate that it is possible to find rando... 详细信息
来源: 评论
Compositional Mixture Representations for vision and Text
Compositional Mixture Representations for Vision and Text
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Alaniz, Stephan Federici, Marco Akata, Zeynep Univ Tubingen Tubingen Germany Max Planck Inst Informat Saarbrucken Germany Univ Amsterdam Amsterdam Netherlands
Learning a common representation space between vision and language allows deep networks to relate objects in the image to the corresponding semantic meaning. We present a model that learns a shared Gaussian mixture re... 详细信息
来源: 评论
Variational Autoencoders for Generating Hyperspectral Imaging Honey Adulteration Data
Variational Autoencoders for Generating Hyperspectral Imagin...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Phillips, Tessa Abdulla, Waleed Univ Auckland Auckland New Zealand
Honey fraud and adulteration are an increasing concern globally. Hyperspectral imaging and machine learning can detect adulterated honey within a known set of honey, where we have captured data at different sugar conc... 详细信息
来源: 评论
Proposal-free Lidar Panoptic Segmentation with Pillar-level Affinity
Proposal-free Lidar Panoptic Segmentation with Pillar-level ...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Chen, Qi Vora, Sourabh Johns Hopkins Univ Baltimore MD 21218 USA Motional Boston MA USA
We propose a simple yet effective proposal-free architecture for lidar panoptic segmentation. We jointly optimize both semantic segmentation and class-agnostic instance classification in a single network using a pilla... 详细信息
来源: 评论
Fuse-PN: A Novel Architecture for Anomaly pattern Segmentation in Aerial Agricultural Images
Fuse-PN: A Novel Architecture for Anomaly Pattern Segmentati...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Innani, Shubham Dutande, Prasad Baheti, Bhakti Talbar, Sanjay Baid, Ujjwal SGGS Inst Engn & Technol Ctr Excellence Signal & Image Proc Nanded 431606 India
Deep learning and pattern recognition in smart farming has seen rapid growth as a building bridge between crop science and computer vision. One of the important application is anomaly segmentation in agriculture like ... 详细信息
来源: 评论
Anomaly Detection in Autonomous Driving: A Survey
Anomaly Detection in Autonomous Driving: A Survey
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Bogdoll, Daniel Nitsche, Maximilian Zoellner, J. Marius FZI Res Ctr Informat Technol Karlsruhe Germany KIT Karlsruhe Inst Technol Karlsruhe Germany
Nowadays, there are outstanding strides towards a future with autonomous vehicles on our roads. While the perception of autonomous vehicles performs well under closed-set conditions, they still struggle to handle the ... 详细信息
来源: 评论
Revisiting The Evaluation of Class Activation Mapping for Explainability: A Novel Metric and Experimental Analysis
Revisiting The Evaluation of Class Activation Mapping for Ex...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Poppi, Samuele Cornia, Marcella Baraldi, Lorenzo Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy
As the request for deep learning solutions increases, the need for explainability is even more fundamental. In this setting, particular attention has been given to visualization techniques, that try to attribute the r... 详细信息
来源: 评论
BCNN: A Binary CNNWith All Matrix Ops Quantized To 1 Bit Precision
BCNN: A Binary CNNWith All Matrix Ops Quantized To 1 Bit Pre...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Redfern, Arthur J. Zhu, Lijun Newquist, Molly K. Texas Instruments Inc 12500 TI Blvd Dallas TX 75243 USA Georgia Inst Technol North Ave NW Atlanta GA 30332 USA
This paper describes a CNN where all CNN style 2D convolution operations that lower to matrix matrix multiplication are fully binary. The network is derived from a common building block structure that is consistent wi... 详细信息
来源: 评论
Towards Domain-Specific Explainable AI: Model Interpretation of a Skin Image Classifier using a Human Approach
Towards Domain-Specific Explainable AI: Model Interpretation...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Stieler, Fabian Rabe, Fabian Bauer, Bernhard Univ Augsburg Augsburg Germany
Machine Learning models have started to outperform medical experts in some classification tasks. Meanwhile, the question of how these classifiers produce certain results is attracting increasing research attention. Cu... 详细信息
来源: 评论