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检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"

共 22906 条记录，以下是4671-4680 订阅

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Lifelong Person Re-Identification via Adaptive Knowledge Accumulation

Lifelong Person Re-Identification via Adaptive Knowledge Acc...

引用

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

作者： Pu, Nan Chen, Wei Liu, Yu Bakker, Erwin M. Lew, Michael S. Leiden Univ LIACS Media Lab Leiden Netherlands Dalian Univ Technol Int Sch Informat Sci & Engn Dalian Peoples R China

ISBN: (纸本)9781665445092

Person re-identification (ReID) methods always learn through a stationary domain that is fixed by the choice of a given dataset. In many contexts (e.g., lifelong learning), those methods are ineffective because the domain is continually changing in which case incremental learning over multiple domains is required potentially. In this work we explore a new and challenging ReID task, namely lifelong person re-identification (LReID), which enables to learn continuously across multiple domains and even generalise on new and unseen domains. Following the cognitive processes in the human brain, we design an Adaptive Knowledge Accumulation (AKA) framework that is endowed with two crucial abilities: knowledge representation and knowledge operation. Our method alleviates catastrophic forgetting on seen domains and demonstrates the ability to generalize to unseen domains. Correspondingly, we also provide a new and large-scale benchmark for LReID. Extensive experiments demonstrate our method outperforms other competitors by a margin of 5.8% mAP in generalising evaluation.

关键词： Training Visualization computer vision Adaptation models Codes Cognitive processes Knowledge representation

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Multiplicative Fourier Level of Detail

Multiplicative Fourier Level of Detail

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

作者： Dou, Yishun Zheng, Zhong Jin, Qiaoqiao Ni, Bingbing Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China Huawei Shenzhen Peoples R China

ISBN: (纸本)9798350301298

We develop a simple yet surprisingly effective implicit representing scheme called Multiplicative Fourier Level of Detail (MFLOD) motivated by the recent success of multiplicative filter network. Built on multi-resolution feature grid/volume (e.g., the sparse voxel octree), each level's feature is first modulated by a sinusoidal function and then element-wisely multiplied by a linear transformation of previous layer's representation in a layer-to-layer recursive manner, yielding the scale-aggregated encodings for a subsequent simple linear forward to get final output. In contrast to previous hybrid representations relying on interleaved multilevel fusion and nonlinear activation-based decoding, MFLOD could be elegantly characterized as a linear combination of sine basis functions with varying amplitude, frequency, and phase upon the learned multilevel features, thus offering great feasibility in Fourier analysis. Comprehensive experimental results on implicit neural representation learning tasks including image fitting, 3D shape representation, and neural radiance fields well demonstrate the superior quality and generalizability achieved by the proposed MFLOD scheme.

关键词： vision + graphics

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Confidence Propagation Cluster: Unleash Full Potential of Object Detectors

Confidence Propagation Cluster: Unleash Full Potential of Ob...

引用

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

作者： Shen, Yichun Jiang, Wanli Xu, Zhen Li, Rundong Kwon, Junghyun Li, Siyi NVIDIA Santa Clara CA 95051 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

It's been a long history that most object detection methods obtain objects by using the non-maximum suppression (NMS) and its improved versions like Soft-NMS to remove redundant bounding boxes. We challenge those NMS-based methods from three aspects: 1) The bounding box with highest confidence value may not be the true positive having the biggest overlap with the ground-truth box. 2) Not only suppression is required for redundant boxes, but also confidence enhancement is needed for those true positives. 3) Sorting candidate boxes by confidence values is not necessary so that full parallelism is achievable. In this paper, inspired by belief propagation (BP), we propose the Confidence Propagation Cluster (CP-Cluster) to replace NMS-based methods, which is fully parallelizable as well as better in accuracy. In CP-Cluster, we borrow the message passing mechanism from BP to penalize redundant boxes and enhance true positives simultaneously in an iterative way until convergence. We verified the effectiveness of CP-Cluster by applying it to various mainstream detectors such as FasterRCNN, SSD, FCOS, YOLOv3, YOLOv5, Centernet etc. Experiments on MS COCO show that our plug and play method, without retraining detectors, is able to steadily improve average mAP of all those state-of-the-art models with a clear margin from 0.3 to 1.9 respectively when compared with NMS-based methods.

关键词： computer vision Message passing Robot vision systems Detectors Object detection Parallel processing pattern recognition

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Cascaded Human-Object Interaction recognition

Cascaded Human-Object Interaction Recognition

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

作者： Zhou, Tianfei Wang, Wenguan Qi, Siyuan Ling, Haibin Shen, Jianbing Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates Swiss Fed Inst Technol Zurich Switzerland Google Mountain View CA 94043 USA SUNY Stony Brook Stony Brook NY 11794 USA

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171685

Rapid progress has been witnessed for human-object interaction (HOI) recognition, but most existing models are confined to single-stage reasoning pipelines. Considering the intrinsic complexity of the task, we introduce a cascade architecture for a multi-stage, coarse-to-fine HOI understanding. At each stage, an instance localization network progressively refines HOI proposals and feeds them into an interaction recognition network. Each of the two networks is also connected to its predecessor at the previous stage, enabling cross-stage information propagation. The interaction recognition network has two crucial parts: a relation ranking module for high-quality HOI proposal selection and a triple-stream classifier for relation prediction. With our carefully-designed human-centric relation features, these two modules work collaboratively towards effective interaction understanding. Further beyond relation detection on a bounding-box level, we make our framework flexible to perform fine-grained pixel-wise relation segmentation;this provides a new glimpse into better relation modeling. Our approach reached the 1st place in the ICCV2019 Person in Context Challenge, on both relation detection and segmentation tasks. It also shows promising results on V-COCO.

关键词： Semantics Task analysis Pipelines computer architecture Proposals Visualization computer vision

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All You Need is Beyond a Good Init: Exploring Better Solution for Training Extremely Deep Convolutional Neural Networks with Orthonormality and Modulation 30

All You Need is Beyond a Good Init: Exploring Better Solutio...

引用

30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Xie, Di Xiong, Jiang Pu, Shiliang Hikvis Res Inst Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781538604571

Deep neural network is difficult to train and this predicament becomes worse as the depth increases. The essence of this problem exists in the magnitude of backpropagated errors that will result in gradient vanishing or exploding phenomenon. We show that a variant of regularizer which utilizes orthonormality among different filter banks can alleviate this problem. Moreover, we design a backward error modulation mechanism based on the quasi-isometry assumption between two consecutive parametric layers. Equipped with these two ingredients, we propose several novel optimization solutions that can be utilized for training a specific-structured (repetitively triple modules of Conv-BNReLU) extremely deep convolutional neural network (CNN) WITHOUT any shortcuts/identity mappings from scratch. Experiments show that our proposed solutions can achieve distinct improvements for a 44-layer and a 110-layer plain networks on both the CIFAR-10 and ImageNet datasets. Moreover, we can successfully train plain CNNs to match the performance of the residual counterparts. Besides, we propose new principles for designing network structure from the insights evoked by orthonormality. Combined with residual structure, we achieve comparative performance on the ImageNet dataset.

关键词： Modulation Neural networks Training Jacobian matrices Convolution computer vision Network architecture

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Single-Sample Face recognition with Image Corruption and Misalignment via Sparse Illumination Transfer

Single-Sample Face Recognition with Image Corruption and Mis...

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26th ieee conference on computer vision and pattern recognition (CVPR)

作者： Zhuang, Liansheng Yang, Allen Y. Zhou, Zihan Sastry, S. Shankar Ma, Yi Univ Sci & Technol China Hefei 230026 Peoples R China Univ Calif Berkeley Dept EECS Berkeley CA 94720 USA Univ Illinois Dept ECE Chicago IL 60680 USA

ISBN: (纸本)9780769549897

Single-sample face recognition is one of the most challenging problems in face recognition. We propose a novel face recognition algorithm to address this problem based on a sparse representation based classification (SRC) framework. The new algorithm is robust to image misalignment and pixel corruption, and is able to reduce required training images to one sample per class. To compensate the missing illumination information typically provided by multiple training images, a sparse illumination transfer (SIT) technique is introduced. The SIT algorithms seek additional illumination examples of face images from one or more additional subject classes, and form an illumination dictionary. By enforcing a sparse representation of the query image, the method can recover and transfer the pose and illumination information from the alignment stage to the recognition stage. Our extensive experiments have demonstrated that the new algorithms significantly outperform the existing algorithms in the single-sample regime and with less restrictions. In particular, the face alignment accuracy is comparable to that of the well-known Deformable SRC algorithm using multiple training images;and the face recognition accuracy exceeds those of the SRC and Extended SRC algorithms using hand labeled alignment initialization.

关键词： computer vision face recognition image classification image representation image resolution image retrieval lighting pose estimation

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Open-Vocabulary Object Detection Using Captions

Open-Vocabulary Object Detection Using Captions

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

作者： Zareian, Alireza Dela Rosa, Kevin Hu, Derek Hao Chang, Shih-Fu Snap Inc Seattle WA 98121 USA Columbia Univ New York NY 10027 USA

ISBN: (纸本)9781665445092

Despite the remarkable accuracy of deep neural networks in object detection, they are costly to train and scale due to supervision requirements. Particularly, learning more object categories typically requires proportionally more bounding box annotations. Weakly supervised and zero-shot learning techniques have been explored to scale object detectors to more categories with less supervision, but they have not been as successful and widely adopted as supervised models. In this paper, we put forth a novel formulation of the object detection problem, namely open-vocabulary object detection, which is more general, more practical, and more effective than weakly supervised and zero-shot approaches. We propose a new method to train object detectors using bounding box annotations for a limited set of object categories, as well as image-caption pairs that cover a larger variety of objects at a significantly lower cost. We show that the proposed method can detect and localize objects for which no bounding box annotation is provided during training, at a significantly higher accuracy than zero-shot approaches. Meanwhile, objects with bounding box annotation can be detected almost as accurately as supervised methods, which is significantly better than weakly supervised baselines. Accordingly, we establish a new state of the art for scalable object detection.

关键词： Location awareness Training Deep learning computer vision Costs Annotations Object detection

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Pixel-aligned Volumetric Avatars

Pixel-aligned Volumetric Avatars

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

作者： Raj, Amit Zollhofer, Michael Simon, Tomas Saragih, Jason Saito, Shunsuke Hays, James Lombardi, Stephen Georgia Inst Technol Atlanta GA 30332 USA Facebook Real Labs Res Menlo Pk CA USA

ISBN: (纸本)9781665445092

Acquisition and rendering of photo-realistic human heads is a highly challenging research problem of particular importance for virtual telepresence. Currently, the highest quality is achieved by volumetric approaches trained in a person-specific manner on multi-view data. These models better represent fine structure, such as hair, compared to simpler mesh-based models. Volumetric models typically employ a global code to represent facial expressions, such that they can be driven by a small set of animation parameters. While such architectures achieve impressive rendering quality, they can not easily be extended to the multi-identity setting. In this paper, we devise a novel approach for predicting volumetric avatars of the human head given just a small number of inputs. We enable generalization across identities by a novel parameterization that combines neural radiance fields with local, pixel-aligned features extracted directly from the inputs, thus side-stepping the need for very deep or complex networks. Our approach is trained in an end-to-end manner solely based on a photometric re-rendering loss without requiring explicit 3D supervision. We demonstrate that our approach outperforms the existing state of the art in terms of quality and is able to generate faithful facial expressions in a multi-identity setting.

关键词： Hair computer vision Head Three-dimensional displays Telepresence Avatars Neural networks

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VIGOR: Cross-View Image Geo-localization beyond One-to-one Retrieval

VIGOR: Cross-View Image Geo-localization beyond One-to-one R...

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

作者： Zhu, Sijie Yang, Taojiannan Chen, Chen Univ North Carolina Charlotte Charlotte NC 28223 USA

ISBN: (纸本)9781665445092

Cross-view image geo-localization aims to determine the locations of street-view query images by matching with GPS-tagged reference images from aerial view. Recent works have achieved surprisingly high retrieval accuracy on city-scale datasets. However, these results rely on the assumption that there exists a reference image exactly centered at the location of any query image, which is not applicable for practical scenarios. In this paper, we redefine this problem with a more realistic assumption that the query image can be arbitrary in the area of interest and the reference images are captured before the queries emerge. This assumption breaks the one-to-one retrieval setting of existing datasets as the queries and reference images are not perfectly aligned pairs, and there may be multiple reference images covering one query location. To bridge the gap between this realistic setting and existing datasets, we propose a new large-scale benchmark -VIGOR- for cross-View Image Geo-localization beyond One-to-one Retrieval. We benchmark existing state-of-the-art methods and propose a novel end-to-end framework to localize the query in a coarse-to-fine manner. Apart from the image-level retrieval accuracy, we also evaluate the localization accuracy in terms of the actual distance (meters) using the raw GPS data. Extensive experiments are conducted under different application scenarios to validate the effectiveness of the proposed method. The results indicate that cross-view geo-localization in this realistic setting is still challenging, fostering new research in this direction. Our dataset and code will be released at https : //***/Jeff - Zilence/VIGOR.

关键词： Location awareness Meters computer vision Codes Benchmark testing pattern recognition Global Positioning System

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Robust feature selection in resting-state fMRI connectivity based on population studies

Robust feature selection in resting-state fMRI connectivity ...

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2010 ieee computer society conference on computer vision and pattern recognition - Workshops, CVPRW 2010

作者： Venkataraman, Archana Kubicki, Marek Westin, Carl-Fredrik Golland, Polina MIT Computer Science Artificial Intelligence Laboratory Cambridge MA 02139 United States Psychiatry Neuroimaging Laboratory Harvard Medical School Boston MA 02215 United States Laboratory for Mathematical Imaging Harvard Medical School Boston MA 02215 United States

ISBN: (纸本)9781424470297

We propose an alternative to univariate statistics for identifying population differences in functional connectivity. Our feature selection method is based on a procedure that searches across subsets of the data to isolate a set of robust, predictive functional connections. The metric, known as the Gini Importance, also summarizes multivariate patterns of interaction, which cannot be captured by univariate techniques. We compare the Gini Importance with univariate statistical tests to evaluate functional connectivity changes induced by schizophrenia. Our empirical results indicate that univariate features vary dramatically across subsets of the data and have little classification power. In contrast, relevant features based on Gini Importance are considerably more stable and allow us to accurately predict the diagnosis of a test subject. © 2010 ieee.

关键词： Population statistics

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