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检索条件"任意字段=IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops"
12857 条 记 录,以下是4921-4930 订阅
排序:
RPSRNet: End-to-End Trainable Rigid Point Set Registration Network using Barnes-Hut 2D-Tree Representation
RPSRNet: End-to-End Trainable Rigid Point Set Registration N...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Ali, Sk Aziz Kahraman, Kerem Reis, Gerd Stricker, Didier TU Kaiserslautern Kaiserslautern Germany German Res Ctr Artificial Intelligence DFKI GmbH Kaiserslautern Germany
We propose RPSRNet - a novel end-to-end trainable deep neural network for rigid point set registration. For this task, we use a novel 2D-tree representation for the input point sets and a hierarchical deep feature emb... 详细信息
来源: 评论
Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction
Dynamic Neural Radiance Fields for Monocular 4D Facial Avata...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Gafni, Guy Thies, Justus Zollhoefer, Michael Niessner, Matthias Tech Univ Munich Munich Germany Facebook Real Labs Res Pittsburgh PA USA
We present dynamic neural radiance fields for modeling the appearance and dynamics of a human face(1). Digitally modeling and reconstructing a talking human is a key building-block for a variety of applications. Espec... 详细信息
来源: 评论
Reciprocal Transformations for Unsupervised Video Object Segmentation
Reciprocal Transformations for Unsupervised Video Object Seg...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Ren, Sucheng Liu, Wenxi Liu, Yongtuo Chen, Haoxin Han, Guoqiang He, Shengfeng South China Univ Technol Sch Comp Sci & Engn Guangzhou Peoples R China Fuzhou Univ Coll Math & Comp Sci Fuzhou Peoples R China
Unsupervised video object segmentation (UVOS) aims at segmenting the primary objects in videos without any human intervention. Due to the lack of prior knowledge about the primary objects, identifying them from videos... 详细信息
来源: 评论
Exploring and Utilizing pattern Imbalance
Exploring and Utilizing Pattern Imbalance
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conference on computer vision and pattern recognition (CVPR)
作者: Shibin Mei Chenglong Zhao Shengchao Yuan Bingbing Ni Shanghai Jiao Tong University Shanghai China
In this paper, we identify pattern imbalance from several aspects, and further develop a new training scheme to avert pattern preference as well as spurious correlation. In contrast to prior methods which are mostly c...
来源: 评论
Multi-Label Learning from Single Positive Labels
Multi-Label Learning from Single Positive Labels
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Cole, Elijah Mac Aodha, Oisin Lorieul, Titouan Perona, Pietro Morris, Dan Jojic, Nebojsa CALTECH Pasadena CA 91125 USA Univ Edinburgh Edinburgh Midlothian Scotland INRIA Rocquencourt France Microsoft AI Earth Washington DC USA Microsoft Res Redmond WA USA
Predicting all applicable labels for a given image is known as multi-label classification. Compared to the standard multi-class case (where each image has only one label), it is considerably more challenging to annota... 详细信息
来源: 评论
Sequential Graph Convolutional Network for Active Learning
Sequential Graph Convolutional Network for Active Learning
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Caramalau, Razvan Bhattarai, Binod Kim, Tae-Kyun Imperial Coll London London England Korea Adv Inst Sci & Technol Daejeon South Korea
We propose a novel pool-based Active Learning framework constructed on a sequential Graph Convolution Network (GCN). Each images feature from a pool of data represents a node in the graph and the edges encode their si... 详细信息
来源: 评论
Informative and Consistent Correspondence Mining for Cross-Domain Weakly Supervised Object Detection
Informative and Consistent Correspondence Mining for Cross-D...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Hou, Luwei Zhang, Yu Fu, Kui Li, Jia Beihang Univ Sch Comp Sci & Engn State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China SenseTime Res Beijing Peoples R China
Cross-domain weakly supervised object detection aims to adapt object-level knowledge from a fully labeled source domain dataset (i.e., with object bounding boxes) to train object detectors for target domains that are ... 详细信息
来源: 评论
VarifocalNet: An IoU-aware Dense Object Detector
VarifocalNet: An IoU-aware Dense Object Detector
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Zhang, Haoyang Wang, Ying Dayoub, Feras Sunderhauf, Niko Queensland Univ Technol Australian Ctr Robot Vis Brisbane Qld Australia Univ Queensland Brisbane Qld Australia
Accurately ranking the vast number of candidate detections is crucial for dense object detectors to achieve high performance. Prior work uses the classification score or a combination of classification and predicted l... 详细信息
来源: 评论
Deep Compositional Metric Learning
Deep Compositional Metric Learning
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Zheng, Wenzhao Wang, Chengkun Lu, Jiwen Zhou, Jie Tsinghua Univ Dept Automat Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China
In this paper, we propose a deep compositional metric learning (DCML) framework for effective and generalizable similarity measurement between images. Conventional deep metric learning methods minimize a discriminativ... 详细信息
来源: 评论
ACTION-Net: Multipath Excitation for Action recognition
ACTION-Net: Multipath Excitation for Action Recognition
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Wang, Zhengwei She, Qi Smolic, Aljosa Trinity Coll Dublin V SENSE Dublin Ireland ByteDance AI Lab Beijing Peoples R China
Spatial-temporal, channel-wise, and motion patterns are three complementary and crucial types of information for video action recognition. Conventional 2D CNNs are computationally cheap but cannot catch temporal relat... 详细信息
来源: 评论