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检索条件"任意字段=IEEE-Computer-Society Conference on Computer Vision and Pattern Recognition Workshops"

共 8962 条记录，以下是1761-1770 订阅

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VoronoiNet General Functional Approximators with Local Support

VoronoiNet General Functional Approximators with Local Suppo...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Williams, Francis Parent-Levesque, Jerome Nowrouzezahrai, Derek Panozzo, Daniele Yi, Kwang Moo Tagliasacchi, Andrea NYU New York NY 10003 USA McGill Univ Montreal PQ Canada Univ Victoria Victoria BC Canada Google Brain Mountain View CA USA

ISBN: (纸本)9781728193601

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： computer vision

来源：评论

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SomethingFinder: Localizing undefined regions using referring expressions

SomethingFinder: Localizing undefined regions using referrin...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Eum, Sungmin Han, David Briggs, Gordon US Army Res Lab Adelphi MD 20783 USA Booz Allen Hamilton Mclean VA 22102 USA US Naval Res Lab Washington DC USA

ISBN: (纸本)9781728193601

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： computer vision

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Simplifying Transformations for a Family of Elastic Metrics on the Space of Surfaces

Simplifying Transformations for a Family of Elastic Metrics ...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Su, Zhe Bauer, Martin Klassen, Eric Gallivan, Kyle Florida State Univ Dept Math Tallahassee FL 32306 USA

ISBN: (纸本)9781728193601

We define a new representation for immersed surfaces in R-3 by combining the SRNF and the induced surface metric. Using the L-2 metric on the space of SRNFs and the DeWitt metric on the space of surface metrics, we obtain a 3-parameter family of metrics that corresponds to the family of "elastic metrics" proposed by Jermyn et al. in [19] on the space of immersed surfaces. Similar to the original SRNF representation this new representation results in an extrinsic distance function on the space of immersed surfaces that is easy to compute as it is given by an explicit formula. In addition to avoiding the degeneracy of the SRNF it allows for a data-driven choice of the parameters of the metric, while still providing for fast and accurate registration of surfaces.

关键词： computer vision

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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 Vision...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Artemyev, Mikhail Churikova, Marina Grinenko, Mikhail Perepelkina, Olga Neurodata Lab LLC Miami FL 33137 USA Lomonosov Moscow State Univ Fac Biol Dept Higher Nervous Act Moscow Russia

ISBN: (纸本)9781728193601

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 spatio-temporal 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： computer vision

来源：评论

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Generalized Class Incremental Learning

Generalized Class Incremental Learning

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Mi, Fei Kong, Lingjing Lin, Tao Yu, Kaicheng Faltings, Boi Ecole Polytech Fed Lausanne EPFL Lausanne Switzerland

ISBN: (纸本)9781728193601

Many real-world machine learning systems require the ability to continually learn new knowledge. Class incremental learning receives increasing attention recently as a solution towards this goal. However, existing methods often introduce some assumptions to simplify the problem setting, which rarely holds in real-world scenarios. In this paper, we formulate a Generalized Class Incremental Learning (GCIL) framework to systematically alleviate these restrictions, and introduce several novel realistic incremental learning scenarios. In addition, we propose a simple yet effective method, namely ReMix, which combines Exemplar Replay (ER) and Mixup to deal with different challenges in realistic GCIL setups. We demonstrate on CIFAR-100 that ReMix outperforms the state-of-the-art methods in different GCIL setups by significant margins without introducing additional computation cost.

关键词： computer vision

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Area Under the ROC Curve Maximization for Metric Learning

Area Under the ROC Curve Maximization for Metric Learning

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Bojana Gajić Ariel Amato Ramon Baldrich Joost van de Weijer Carlo Gatta Vintra Inc. Barcelona Spain Computer Vision Center Barcelona Spain

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Most popular metric learning losses have no direct relation with the evaluation metrics that are subsequently applied to evaluate their performance. We hypothesize that training a metric learning model by maximizing the area under the ROC curve (which is a typical performance measure of recognition systems) can induce an implicit ranking suitable for retrieval problems. This hypothesis is supported by previous work that proved that a curve dominates in ROC space if and only if it dominates in Precision-Recall space. To test this hypothesis, we design and maximize an approximated, derivable relaxation of the area under the ROC curve. The proposed AUC loss achieves state-of-the-art results on two large scale retrieval benchmark datasets (Stanford Online Products and DeepFashion In-Shop). Moreover, the AUC loss achieves comparable performance to more complex, domain specific, state-of-the-art methods for vehicle re-identification.

关键词： Training computer vision conferences Area measurement Benchmark testing pattern recognition

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End-to-end Optimized Video Compression with MV-Residual Prediction

End-to-end Optimized Video Compression with MV-Residual Pred...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wu, XiangJi Zhang, Ziwen Feng, Jie Zhou, Lei Wu, Junmin Tucodec Inc Shanghai Peoples R China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

We present an end-to-end trainable framework for P-frame compression in this paper. A joint motion vector (MV) and residual prediction network MV-Residual is designed to extract the ensembled features of motion representations and residual information by treating the two successive frames as inputs. The prior probability of the latent representations is modeled by a hyperprior auto-encoder and trained jointly with the MV-Residual network. Specially, the spatially-displaced convolution is applied for video frame prediction, in which a motion kernel for each pixel is learned to generate predicted pixel by applying the kernel at a displaced location in the source image. Finally, novel rate allocation and post-processing strategies are used to produce the final compressed bits, considering the bits constraint of the challenge. The experimental results on validation set show that the proposed optimized framework can generate the highest MS-SSIM for P-frame compression competition.

关键词： Image coding Video compression Convolution Kernel computer vision conferences pattern recognition

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Transfering Low-Frequency Features for Domain Adaptation

Transfering Low-Frequency Features for Domain Adaptation

引用

2022 ieee International conference on Multimedia and Expo, ICME 2022

作者： Li, Zhaowen Zhao, Xu Zhao, Chaoyang Tang, Ming Wang, Jinqiao Institute of Automation Chinese Academy of Sciences National Laboratory of Pattern Recognition Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Development Research Institute of Guangzhou Smart City China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Previous unsupervised domain adaptation methods did not handle the cross-domain problem from the perspective of frequency for computer vision. The images or feature maps of different domains can be decomposed into the low-frequency component and high-frequency component. This paper pro-poses the assumption that low-frequency information is more domain-invariant while the high-frequency information con-tains domain-related information. Hence, we introduce an approach, named low-frequency module (LFM), to extract domain-invariant feature representations. The LFM is constructed with the digital Gaussian low-pass filter. Our method is easy to implement and introduces no extra hyperparame-ter. We design two effective ways to utilize the LFM for domain adaptation, and our method is complementary to other existing methods and formulated as a plug-and-play unit that can be combined with these methods. Experimental results demonstrate that our LFM outperforms state-of-the-art meth-ods for various computer vision tasks, including image clas-sification and object detection. © 2022 ieee.

关键词： computer vision

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BoxInst: High-Performance Instance Segmentation with Box Annotations

BoxInst: High-Performance Instance Segmentation with Box Ann...

引用

2021 ieee/CVF conference on computer vision and pattern recognition, CVPR 2021

作者： Tian, Zhi Shen, Chunhua Wang, Xinlong Chen, Hao The University of Adelaide Australia

ISBN: (纸本)9781665445092

We present a high-performance method that can achieve mask-level instance segmentation with only bounding-box annotations for training. While this setting has been studied in the literature, here we show significantly stronger performance with a simple design (e.g., dramatically improving previous best reported mask AP of 21.1% [13] to 31.6% on the COCO dataset). Our core idea is to redesign the loss of learning masks in instance segmentation, with no modification to the segmentation network itself. The new loss functions can supervise the mask training without relying on mask annotations. This is made possible with two loss terms, namely, 1) a surrogate term that minimizes the discrepancy between the projections of the ground-truth box and the predicted mask;2) a pairwise loss that can exploit the prior that proximal pixels with similar colors are very likely to have the same category label. Experiments demonstrate that the redesigned mask loss can yield surprisingly high-quality instance masks with only box annotations. For example, without using any mask annotations, with a ResNet-101 backbone and 3× training schedule, we achieve 33.2% mask AP on COCO test-dev split (vs. 39.1% of the fully supervised counterpart). Our excellent experiment results on COCO and Pascal VOC indicate that our method dramatically narrows the performance gap between weakly and fully supervised instance segmentation. Code is available at: https://***/AdelaiDet. © 2021 ieee

关键词： computer vision

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CPARR: Category-based Proposal Analysis for Referring Relationships

CPARR: Category-based Proposal Analysis for Referring Relati...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： He, Chuanzi Zhu, Haidong Gao, Jiyang Chen, Kan Nevatia, Ram Univ Southern Calif Los Angeles CA 90007 USA

ISBN: (纸本)9781728193601

The task of referring relationships is to localize subject and object entities in an image satisfying a relationship query, which is given in the form of . This requires simultaneous localization of the subject and object entities in a specified relationship. We introduce a simple yet effective proposal-based method for referring relationships. Different from the existing methods such as SSAS, our method can generate a high-resolution result while reducing its complexity and ambiguity. Our method is composed of two modules: a category-based proposal generation module to select the proposals related to the entities and a predicate analysis module to score the compatibility of pairs of selected proposals. We show state-of-the-art performance on the referring relationship task on two public datasets: Visual Relationship Detection and Visual Genome.

关键词： computer vision

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