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检索条件"任意字段=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003"

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Multi-level 3D CNN for Learning Multi-scale Spatial Features 32

Multi-level 3D CNN for Learning Multi-scale Spatial Features

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Ghadai, Sambit Lee, Xian Yeow Balu, Aditya Sarkar, Soumik Krishnamurthy, Adarsh Iowa State Univ Ames IA 50011 USA

ISBN: (纸本)9781728125060

3D object recognition accuracy can be improved by learning the multi-scale spatial features from 3D spatial geometric representations of objects such as point clouds, 3D models, surfaces, and RGB-D data. Current deep learning approaches learn such features either using structured data representations (voxel grids and octrees) or from unstructured representations (graphs and point clouds). Learning features from such structured representations is limited by the restriction on resolution and tree depth while unstructured representations creates a challenge due to non uniformity among data samples. In this paper, we propose an end-to-end multi-level learning approach on a multi-level voxel grid to overcome these drawbacks. To demonstrate the utility of the proposed multi-level learning, we use a multi-level voxel representation of 3D objects to perform object recognition. The multi-level voxel representation consists of a coarse voxel grid that contains volumetric information of the 3D object. In addition, each voxel in the coarse grid that contains a portion of the object boundary is subdivided into multiple fine-level voxel grids. The performance of our multi-level learning algorithm for object recognition is comparable to dense voxel representations while using significantly lower memory.

关键词： Object recognition

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Privacy-Preserving Action recognition using Coded Aperture Videos 32

Privacy-Preserving Action Recognition using Coded Aperture V...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wang, Zihao W. Vineet, Vibhav Pittaluga, Francesco Sinha, Sudipta N. Cossairt, Oliver Kang, Sing Bing Northwestern Univ Evanston IL 60208 USA Microsoft Res Redmond WA USA Univ Florida Gainesville FL 32611 USA Zillow Grp Seattle WA USA

ISBN: (纸本)9781728125060

The risk of unauthorized remote access of streaming video from networked cameras underlines the need for stronger privacy safeguards. We propose a lens free coded aperture camera system for human action recognition that is privacy-preserving. While coded aperture systems exist, we believe ours is the first system designed for action recognition without the need for image restoration as an intermediate step. Action recognition is done using a deep network that takes in as input, non-invertible motion features between pairs offrames computed using phase correlation and log-polar transformation. Phase correlation encodes translation while the log polar transformation encodes in plane rotation and scaling. We show that the translation features are independent of the coded aperture design, as long as its spectral response within the bandwidth has no zeros. Stacking motion features computed on frames at multiple different strides in the video can improve accuracy. Preliminary results on simulated data based on a subset of the UCF and NTU datasets are promising. We also describe our prototype lens free coded aperture camera system, and results for real captured videos are mixed.

关键词： Image reconstruction

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Patch-based Discriminative Feature Learning for Unsupervised Person Re-identification 32

Patch-based Discriminative Feature Learning for Unsupervised...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Yang, Qize Yu, Hong-Xing Wu, Ancong Zheng, Wei-Shi Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Guangdong Peoples R China Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou Guangdong Peoples R China Accuvis Technol Co Ltd Zhunan Town Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Beijing Peoples R China

ISBN: (纸本)9781728132938

While discriminative local features have been shown effective in solving the person re-identification problem, they are limited to be trained on fully pairwise labelled data which is expensive to obtain. In this work, we overcome this problem by proposing a patch-based unsupervised learning framework in order to learn discriminative feature from patches instead of the whole images. The patch-based learning leverages similarity between patches to learn a discriminative model. Specifically, we develop a PatchNet to select patches from the feature map and learn discriminative features for these patches. To provide effective guidance for the PatchNet to learn discriminative patch feature on unlabeled datasets, we propose an unsupervised patch-based discriminative feature learning loss. In addition, we design an image-level feature learning loss to leverage all the patch features of the same image to serve as an image-level guidance for the PatchNet. Extensive experiments validate the superiority of our method for unsupervised person re-id. Our code is available at https://***/QizeYang/PAUL.

关键词： Biometrics Categorization recognition: Detection Retrieval Video Analytics

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FaceGenderID: Exploiting Gender Information in DCNNs Face recognition Systems 32

FaceGenderID: Exploiting Gender Information in DCNNs Face Re...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Vera-Rodriguez, Ruben Blazquez, Marta Morales, Aythami Gonzalez-Sosa, Ester Neves, Joao C. Proenca, Hugo Univ Autonoma Madrid Biometr & Data Pattern Analyt BiDA Lab Madrid Spain Nokia Bell Labs Madrid Spain Univ Beira Interior Inst Telecomun Covilha Portugal

ISBN: (纸本)9781728125060

This paper addresses the effect of gender as a covariate in face verification systems. Even though pre-trained models based on Deep Convolutional Neural Networks (DCNNs), such as liGG-Face or ResNet-50, achieve very high performance, they are (rained on very large datasets comprising millions of images, which have biases regarding demographic aspects like the gender and the ethnicity among others. In this work, we first analyse the separate performance of these state-of-the-art models for males and females. We observe a gap between face verification performances obtained by both gender classes. These results suggest that features obtained by biased models are affected by the gender covariate. We propose a gender-dependent (raining approach to improve the feature representation for both genders, and develop both: i) gender specific DCNNs models, and ii) a gender balanced DCNNs model. Our results show significant and consistent improvements in face verification performance for both genders, individually and in general with our proposed approach. Finally we announce the availability (at GitHubl) of the FaceGenderlD DCNNs models proposed in this work, which can support further experiments on (his topic.

关键词： Large dataset

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Masked Graph Attention Network for Person Re-identification 32

Masked Graph Attention Network for Person Re-identification

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Bao, Liqiang Ma, Bingpeng Chang, Hong Chen, Xilin Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc CAS Beijing 100190 Peoples R China

ISBN: (纸本)9781728125060

The mainstream methods for person re-identification (ReID) mainly focus on the correspondence between individual sample images and labels, while ignoring rich global mutual information resides in the whole sample set. We propose a method called Masked Graph Attention Network (MGAT) to address this problem. MGAT operates on the complete graph constructed with the extracted features, where nodes are able to directionally attend over other nodes' features under the guidance of label information in the form of mask matrix. By using MGAT module, the previously neglected global mutual information is exploited to generate an optimized feature space with more discriminant power. Meanwhile, we propose to feedback the optimization information learned by MGAT module to the feature-embedding network to enhance the mapping capability, thus avoiding the difficulty to handle large-scale graphs in testing phase. To evaluate our method, we conduct experiments on three commonly used ReID datasets. The results show that our method outperforms most mainstream methods, and is highly comparable to the state-of-the-art method.

关键词： computer vision

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Hierarchical Feature-Pair Relation Networks for Face recognition 32

Hierarchical Feature-Pair Relation Networks for Face Recogni...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Kang, Bong-Nam Kim, Yonghyun Jun, Bongjin Kim, Daijin POSTECH Dept Comp Sci & Engn Pohang Si Gyeongsangbuk D South Korea Stradvision Inc Seoul South Korea

ISBN: (纸本)9781728125060

We propose a novel face recognition method using a Hierarchical Feature Relational Network (HFRN) which extracts facial part representations around facial landmark points, and predicts hierarchical latent relations between facial part representations. These hierarchical latent relations should be unique relations within the same identity and discriminative relations among different identities for face recognition task. To do this, the HFRN extracts appearance features as facial parts representations around facial landmark points on the feature maps, globally pool these extracted appearance features onto single feature vectors, and captures the relations for the pairs of appearance features. The HFRN captures the locally detailed relations in the low-level layers and the locally abstracted global relations in the high-level layers for the pairs of appearance features extracted around facial landmark points projected on each layer, respectively. These relations from low-level layers to high-level layers are concatenated into a single hierarchical relation feature. To further improve the accuracy offace recognition, we combine the global appearance feature with the hierarchical relation feature. In experiments, the proposed method achieves the comparable performance in the I :I face verification and I :N face identification tasks compared to existing state-of-the-art methods on the challenging IARPA Janus Benchmark A (IJB-A) and IARPA Janus Benchmark B (IJB-B) datasets.

关键词： Face recognition

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Learning a Controller Fusion Network by Online Trajectory Filtering for vision-based UAV Racing 32

Learning a Controller Fusion Network by Online Trajectory Fi...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Muller, Matthias Li, Guohao Casser, Vincent Smith, Neil Michels, Dominik L. Ghanem, Bernard King Abdullah Univ Sci & Technol Visual Comp Ctr Thuwal Saudi Arabia

ISBN: (纸本)9781728125060

Autonomous UAV racing has recently emerged as an interesting research problem. The dream is to beat humans in this new fast-paced sport. A common approach is to learn an end-to-end policy that directly predicts controls from raw images by imitating an expert. However, such a policy is limited by the expert it imitates and scaling to other environments and vehicle dynamics is difficult. One approach to overcome the drawbacks of an end-to-end policy is to train a network only on the perception task and handle control with a PID or MPC controller. However, a single controller must be extensively tuned and cannot usually cover the whole state space. In this paper, we propose learning an optimized controller using a DNN that fuses multiple controllers. The network learns a robust controller with online trajectory filtering, which suppresses noisy trajectories and imperfections of individual controllers. The result is a network that is able to learn a good fusion of filtered trajectories from different controllers leading to significant improvements in overall performance. We compare our trained network to controllers it has learned from, end-to-end baselines and human pilots in a realistic simulation;our network beats all baselines in extensive experiments and approaches the performance of a professional human pilot.

关键词： Controllers

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RI-GAN: An End-to-End Network for Single Image Haze Removal 32

RI-GAN: An End-to-End Network for Single Image Haze Removal

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Dudhane, Akshay Aulakh, Harshjeet Singh Murala, Subrahmanyam Indian Inst Technol Ropar Comp Vis & Pattern Recognit Lab Rupnagar Punjab India

ISBN: (纸本)9781728125060

The presence of the haze or fog particles in the atmosphere causes visibility degradation in the captured scene. Most of the initial approaches anticipate the transmission map of the hazy scene, airtight component and make use of an atmospheric scattering model to reduce the effect of haze and to recover the haze free scene. In spite of the remarkable progress of these approaches, they propagate cascaded error upstretched due to the employed priors. We embrace this observation and designed an end-to-end generative adversarial network (GAN) for single image haze removal. Proposed network bypasses the intermediate stages and directly recovers the haze free scene. Generator architecture of the proposed network is designed using a novel residual inception (RI) module. Proposed RI module comprises of dense connections within the multi-scale convolution layers which allows it to learn the integrated flavors of the haze-related features. Discriminator of the proposed network is built using the dense residual module. Further, to preserve the edge and the structural details in the recovered haze free scene, structural similarity index and edge loss along with the LI loss are incorporated in the GAN loss. Experimental analysis has been carried out on NTIRE2019 dehazing challenge dataset, D-Hazy [ I and indoor SOTS [ I databases. Experiments on the publically available datasets show that the proposed method outperforms the existing methods for image de-hazing.

关键词： Generative adversarial networks

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Revisiting Depth-based Face recognition from a Quality Perspective 32

Revisiting Depth-based Face Recognition from a Quality Persp...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Hu, Zhenguo Zhao, Qijun Liu, Feng Sichuan Univ Coll Comp Sci Chengdu Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

ISBN: (纸本)9781728125060

Face recognition using depth data has attracted increasing attention from both academia and industry in the past five years. Despite the large number of depth-based face recognition methods in the literature, high quality data are usually required for high recognition accuracy. In this paper, we measure the quality of 3D face data in terms of resolution and precision, and evaluate how the accuracy of three deep face recognition models varies on several benchmark databases as the facial depth data resolution changes from dense to sparse and as the precision changes from high to low. From the experimental results, several observations are made. (i) Given a high precision, a low resolution of 3K is sufficient to represent a 3D face;when the precision decreases, using higher resolutions can benefit face recognition, but the recognition accuracy becomes saturated as the resolution reaches 10K. (ii) Depth precision is more critical than resolution in depth-basedface recognition, and a precision of 1mm is generally preferred as a good balance between accuracy and cost. (iii) The deep models trained with low-quality data perform more stable across data of different quality levels. We believe that these observations are beneficial for both depth sensor manufacturers and depth-basedface recognition system developers.

关键词： Face recognition

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2D-3D Heterogeneous Face recognition based on Deep Coupled Spectral Regression 32

2D-3D Heterogeneous Face Recognition based on Deep Coupled S...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zheng, Yangtao Huang, Di Li, Weixin Wang, Shupeng Wang, Yunhong Beihang Univ Trip Lab Sch Comp Sci & Engn Beijing 100191 Peoples R China

ISBN: (纸本)9781728125060

As one of the major branches in Face recognition (FR), 2D-3D Heterogeneous FR (HFR), where face comparison is achieved across the texture and shape modalities, has become more important. This paper proposes a novel deep learning based end-to -end approach, namely Deep Coupled Spectral Regression (DCSR), for such an issue. It jointly makes use of both the advantages of CNN based deep features and CSR based common subspace. Specifically, from 2D texture and 3D depth face maps, DCSR extracts more powerfulfeatures by a deep network with the cross-modality triplet loss, which show much better uniqueness and robustness than the hand-crafted ones. Further, DCSR learns the shared space between different modalities with the constraints of sample labels, and is thereby more discriminative than the widely used unsupervised methods. More importantly, the two steps above are integrated through a couple layer to explicitly optimize the weights of deep features and projection directions rather than a simple combination. Experiments are carried out on the FRGC v2.0 database, and the results reported clearly demonstrate the competency of our proposed method. Its generalization ability is also validated by additional experiments conducted on the CASIA NIR-VIS 2.0 database.

关键词： Face recognition

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