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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"
30983 条 记 录,以下是4751-4760 订阅
排序:
clDice - a Novel Topology-Preserving Loss Function for Tubular Structure Segmentation
clDice - a Novel Topology-Preserving Loss Function for Tubul...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Shit, Suprosanna Paetzold, Johannes C. Sekuboyina, Anjany Ezhov, Ivan Unger, Alexander Zhylka, Andrey Pluim, Josien P. W. Bauer, Ulrich Menze, Bjoern H. Tech Univ Munich Munich Germany Eindhoven Univ Technol Eindhoven Netherlands
Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or roads, is relevant to many fields of research. For such structures, the topology is their most important characteristic;particula... 详细信息
来源: 评论
StyleMix: Separating Content and Style for Enhanced Data Augmentation
StyleMix: Separating Content and Style for Enhanced Data Aug...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Hong, Minui Choi, Jinwoo Kim, Gunhee Seoul Natl Univ Seoul South Korea
In spite of the great success of deep neural networks for many challenging classification tasks, the learned networks are vulnerable to overfitting and adversarial attacks. Recently, mixup based augmentation methods h... 详细信息
来源: 评论
ScanFormer: Referring Expression Comprehension by Iteratively Scanning
ScanFormer: Referring Expression Comprehension by Iterativel...
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conference on computer vision and pattern recognition (CVPR)
作者: Wei Su Peihan Miao Huanzhang Dou Xi Li College of Computer Science and Technology Zhejiang University School of Software Technology Zhejiang University Zhejiang-Singapore Innovation and Al Joint Research Lab
Referring Expression Comprehension (REC) aims to localize the target objects specified by free-form natural language descriptions in images. While state-of-the-art methods achieve impressive performance, they perform ... 详细信息
来源: 评论
Reciprocal Landmark Detection and Tracking with Extremely Few Annotations
Reciprocal Landmark Detection and Tracking with Extremely Fe...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Lin, Jianzhe Sahebzamani, Ghazal Luong, Christina Dezaki, Fatemeh Taheri Jafari, Mohammad Abolmaesumi, Purang Tsang, Teresa Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada Vancouver Gen Hosp Vancouver BC Canada Univ British Columbia Dept Med Vancouver BC Canada Univ British Columbia Div Cardiol Vancouver BC Canada
Localization of anatomical landmarks to perform two-dimensional measurements in echocardiography is part of routine clinical workflow in cardiac disease diagnosis. Automatic localization of those landmarks is highly d... 详细信息
来源: 评论
MIST: Multiple Instance Spatial Transformer
MIST: Multiple Instance Spatial Transformer
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Angles, Baptiste Jin, Yuhe Kornblith, Simon Tagliasacchi, Andrea Yi, Kwang Moo Univ Victoria Victoria BC Canada Univ British Columbia Vancouver BC Canada Google Res Mountain View CA USA
We propose a deep network that can be trained to tackle image reconstruction and classification problems that involve detection of multiple object instances, without any supervision regarding their whereabouts. The ne... 详细信息
来源: 评论
NeuralFusion: Online Depth Fusion in Latent Space
NeuralFusion: Online Depth Fusion in Latent Space
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Weder, Silvan Schonberger, Johannes L. Pollefeys, Marc Oswald, Martin R. Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland Microsoft Mixed Real & AI Zurich Lab Zurich Switzerland
We present a novel online depth map fusion approach that learns depth map aggregation in a latent feature space. While previous fusion methods use an explicit scene representation like signed distance functions (SDFs)... 详细信息
来源: 评论
Learned Initializations for Optimizing Coordinate-Based Neural Representations
Learned Initializations for Optimizing Coordinate-Based Neur...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Tancik, Matthew Mildenhall, Ben Wang, Terrance Schmidt, Divi Srinivasan, Pratul P. Barron, Jonathan T. Ng, Ren Univ Calif Berkeley Berkeley CA 94720 USA Google Res Mountain View CA USA
Coordinate-based neural representations have shown significant promise as an alternative to discrete, array-based representations for complex low dimensional signals. However, optimizing a coordinate-based network fro... 详细信息
来源: 评论
Gradient-based Algorithms for Machine Teaching
Gradient-based Algorithms for Machine Teaching
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Wang, Pei Nagrecha, Kabir Vasconcelos, Nuno Univ Calif San Diego San Diego CA 92093 USA
The problem of machine teaching is considered. A new formulation is proposed under the assumption of an optimal student, where optimality is defined in the usual machine learning sense of empirical risk minimization. ... 详细信息
来源: 评论
Point Cloud Upsampling via Disentangled Refinement
Point Cloud Upsampling via Disentangled Refinement
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Li, Ruihui Li, Xianzhi Heng, Pheng-Ann Fu, Chi-Wing Chinese Univ Hong Kong Hong Kong Peoples R China
Point clouds produced by 3D scanning are often sparse, non-uniform, and noisy. Recent upsampling approaches aim to generate a dense point set, while achieving both distribution uniformity and proximity-to-surface, and... 详细信息
来源: 评论
ReMix: Towards Image-to-Image Translation with Limited Data
ReMix: Towards Image-to-Image Translation with Limited Data
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Cao, Jie Hou, Luanxuan Yang, Ming-Hsuan He, Ran Sun, Zhenan CASIA CRIPAC NLPR Beijing Peoples R China CASIA CEBSIT Beijing Peoples R China UCAS AIR Beijing Peoples R China Univ Calif Merced Merced CA USA Google Res Mountain View CA USA Yonsei Univ Seoul South Korea
Image-to-image (I2I) translation methods based on generative adversarial networks (GANs) typically suffer from overfitting when limited training data is available. In this work, we propose a data augmentation method (... 详细信息
来源: 评论