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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"
30988 条 记 录,以下是4771-4780 订阅
排序:
Correlated Input-Dependent Label Noise in Large-Scale Image Classification
Correlated Input-Dependent Label Noise in Large-Scale Image ...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Collier, Mark Mustafa, Basil Kokiopoulou, Efi Jenatton, Rodolphe Berent, Jesse Google AI Mountain View CA 94043 USA
Large scale image classification datasets often contain noisy labels. We take a principled probabilistic approach to modelling input-dependent, also known as heteroscedastic, label noise in these datasets. We place a ... 详细信息
来源: 评论
BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
BasicVSR: The Search for Essential Components in Video Super...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Chan, Kelvin C. K. Wang, Xintao Yu, Ke Dong, Chao Loy, Chen Change Nanyang Technol Univ S Lab Singapore Singapore Tencent PCG Appl Res Ctr Shenzhen Peoples R China Chinese Univ Hong Kong CUHK SenseTime Joint Lab Hong Kong Peoples R China Chinese Acad Sci SIAT SenseTime Joint Lab Shenzhen Key Lab Comp Vis & Pattern Recognit Shenzhen Inst Adv Technol Beijing Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc SIAT Branch Shenzhen Peoples R China
Video super-resolution (VSR) approaches tend to have more components than the image counterparts as they need to exploit the additional temporal dimension. Complex designs are not uncommon. In this study, we wish to u... 详细信息
来源: 评论
Beyond Bounding-Box: Convex-hull Feature Adaptation for Oriented and Densely Packed Object Detection
Beyond Bounding-Box: Convex-hull Feature Adaptation for Orie...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Guo, Zonghao Liu, Chang Zhang, Xiaosong Jiao, Jianbin Ji, Xiangyang Ye, Qixiang Univ Chinese Acad Sci Beijing Peoples R China Tsinghua Univ Beijing Peoples R China
Detecting oriented and densely packed objects remains challenging for spatial feature aliasing caused by the intersection of reception fields between objects. In this paper, we propose a convex-hull feature adaptation... 详细信息
来源: 评论
CAGE: Controllable Articulation GEneration
CAGE: Controllable Articulation GEneration
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conference on computer vision and pattern recognition (CVPR)
作者: Jiayi Liu Hou In Ivan Tam Ali Mahdavi-Amiri Manolis Savva Simon Fraser University
We address the challenge of generating 3D articulated objects in a controllable fashion. Currently, modeling artic-ulated 3D objects is either achieved through laborious manual authoring, or using methods from prior w... 详细信息
来源: 评论
Exploring Sparsity in Image Super-Resolution for Efficient Inference
Exploring Sparsity in Image Super-Resolution for Efficient I...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Wang, Longguang Dong, Xiaoyu Wang, Yingqian Ying, Xinyi Lin, Zaiping An, Wei Guo, Yulan Natl Univ Def Technol Changsha Peoples R China Univ Tokyo Tokyo Japan RIKEN AIP Tokyo Japan
Current CNN-based super-resolution (SR) methods process all locations equally with computational resources being uniformly assigned in space. However, since missing details in low-resolution (LR) images mainly exist i... 详细信息
来源: 评论
Weakly Supervised Instance Segmentation for Videos with Temporal Mask Consistency
Weakly Supervised Instance Segmentation for Videos with Temp...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Liu, Qing Ramanathan, Vignesh Mahajan, Dhruv Yuille, Alan Yang, Zhenheng Johns Hopkins Univ Baltimore MD 21218 USA Facebook Menlo Pk CA USA
Weakly supervised instance segmentation reduces the cost of annotations required to train models. However, existing approaches which rely only on image-level class labels predominantly suffer from errors due to (a) pa... 详细信息
来源: 评论
Class-Incremental Experience Replay for Continual Learning under Concept Drift
Class-Incremental Experience Replay for Continual Learning u...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Korycki, Lukasz Krawczyk, Bartosz Virginia Commonwealth Univ Dept Comp Sci Richmond VA 23284 USA
Modern machine learning systems need to be able to cope with constantly arriving and changing data. Two main areas of research dealing with such scenarios are continual learning and data stream mining. Continual learn... 详细信息
来源: 评论
PointNetLK Revisited
PointNetLK Revisited
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Li, Xueqian Pontes, Jhony Kaesemodel Lucey, Simon Argo AI Pittsburgh PA 15222 USA Univ Adelaide Adelaide SA Australia Carnegie Mellon Univ Pittsburgh PA 15213 USA
We address the generalization ability of recent learning-based point cloud registration methods. Despite their success, these approaches tend to have poor performance when applied to mismatched conditions that are not... 详细信息
来源: 评论
Content-aware Input Scaling and Deep Learning Computation Offloading for Low-Latency Embedded vision
Content-aware Input Scaling and Deep Learning Computation Of...
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IEEE computer Society conference on computer vision and pattern recognition Workshops (CVPRW)
作者: Omkar Prabhune Tianen Chen Younghyun Kim Purdue University University of Wisconsin-Madison
Deploying deep learning (DL) models for visual recognition on embedded systems is often constrained by their limited compute power and storage capacity, and has stringent latency and power requirements. As emerging DL... 详细信息
来源: 评论
KOALAnet: Blind Super-Resolution using Kernel-Oriented Adaptive Local Adjustment
KOALAnet: Blind Super-Resolution using Kernel-Oriented Adapt...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Kim, Soo Ye Sim, Hyeonjun Kim, Munchurl Korea Adv Inst Sci & Technol Daejeon South Korea
Blind super-resolution (SR) methods aim to generate a high quality high resolution image from a low resolution image containing unknown degradations. However, natural images contain various types and amounts of blur: ... 详细信息
来源: 评论