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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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Cross-stream Selective Networks for Action recognition 32

Cross-stream Selective Networks for Action Recognition

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32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Pan, Bowen Sun, Jiankai Lin, Wuwei Wang, Limin Lin, Weiyao Shanghai Jiao Tong Univ Shanghai Peoples R China SenseTime Res Hong Kong Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781728125060

Combining multiple information streams has shown obvious improvements in video action recognition. Most existing works handle each stream independently or perform a simple combination on temporally simultaneous samples in multi-streams, which fails to make full use of the stream wise complementary property due to the negligence of the temporal pattern gaps among streams. In this paper, we propose a cross-stream selective network (CSN) to properly integrate and evaluate information in multi-streams. The proposed CSN first introduces a local selective-sampling module (LSM), which can find asynchronous correspondences among streams and construct high-correlated sample groups across multiple information streams. This LSM can effectively deal with the temporal dis-alignment among different streams, leading to a better integration of cross-stream information. We further introduce a global adaptive weighting module (GAM). It adaptively evaluates the importance weights for each cross-stream sample group and selects temporally more important ones in action recognition. With the integration of cross-stream information, our GAM can obtain more reasonable importance than the existing single-stream weighting schemes. Extensive experiments on benchmark datasets of UCF101 and HMDB51 demonstrate the effectiveness of our approach over previous state-of-the-art methods.

关键词： computer vision

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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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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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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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Fine-Grained Predicates Learning for Scene Graph Generation

Fine-Grained Predicates Learning for Scene Graph Generation

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conference on computer vision and pattern recognition (CVPR)

作者： Xinyu Lyu Lianli Gao Yuyu Guo Zhou Zhao Hao Huang Heng Tao Shen Jingkuan Song Center for Future Media & School of Computer Science and Engineering University of Electronic Science and Technology of China China Zhejiang University China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469470

The performance of current Scene Graph Generation models is severely hampered by some hard-to-distinguish predicates, e.g., “woman-on/standing on/walking on-beach” or “woman-near/looking at/in front of-child”. While general SGG models are prone to predict head predicates and existing re-balancing strategies prefer tail categories, none of them can appropriately handle these hard-to-distinguish predicates. To tackle this issue, inspired by fine-grained image classification, which focuses on differentiating among hard-to-distinguish object classes, we propose a method named Fine-Grained Predicates Learning (FGPL) which aims at differentiating among hard-to-distinguish predicates for Scene Graph Generation task. Specifically, we first introduce a Predicate Lattice that helps SGG models to figure out fine-grained predicate pairs. Then, utilizing the Predicate Lattice, we propose a Category Discriminating Loss and an Entity Discriminating Loss, which both contribute to distinguishing fine-grained predicates while maintaining learned discriminatory power over recognizable ones. The proposed model-agnostic strategy significantly boosts the performances of three benchmark models (Transformer, VCTree, and Motif) by 22.8%, 24.1% and 21.7% of Mean Recall (mR@100) on the Predicate Classification sub-task, respectively. Our model also outperforms state-of-the-art methods by a large margin (i.e., 6.1%, 4.6%, and 3.2% of Mean Recall (mR@100)) on the Visual Genome dataset. Codes are publicly available 1 1 https://***/XinyuLyu/FGPL.

关键词： Visualization Head Lattices Tail Benchmark testing Predictive models Transformers

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Learning Raw Image Denoising with Bayer pattern Unification and Bayer Preserving Augmentation 32

Learning Raw Image Denoising with Bayer Pattern Unification ...

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32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Liu, Jiaming Wu, Chi-Hao Wang, Yuzhi Xu, Qin Zhou, Yuqian Huang, Haibin Wang, Chuan Cai, Shaofan Ding, Yifan Fan, Haoqiang Wang, Jue Megvii Technol Beijing Peoples R China Tsinghua Univ Beijing Peoples R China Univ Cent Florida Orlando FL 32816 USA

ISBN: (纸本)9781728125060

In this paper, we present new data pre-processing and augmentation techniques for DNN-based raw image de noising. Compared with traditional RGB image denoising, performing this task on direct camera sensor readings presents new challenges such as how to effectively handle various Bayer patterns from different data sources, and subsequently how to perform valid data augmentation with raw images. To address the first problem, we propose a Bayer pattern unification (BayerUnify) method to unify different Bayer patterns. This allows us to fully utilize a heterogeneous dataset to train a single denoising model instead of training one model for each pattern. Furthermore, while it is essential to augment the dataset to improve model generalization and performance, we discovered that it is error-prone to modify raw images by adapting augmentation methods designed for RGB images. Towards this end, we present a Bayer preserving augmentation (BayerAug) method as an effective approach for raw image augmentation. Combining these data processing technqiues with a modified U-Net, our method achieves a PSNR of 52.11 and a SSIM of 0.9969 in NTIRE 2019 Real Image Denoising Challenge, demonstrating the state-of-the-art performance.

关键词： Image denoising

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DHP19: Dynamic vision Sensor 3D Human Pose Dataset 32

DHP19: Dynamic Vision Sensor 3D Human Pose Dataset

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32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Calabrese, Enrico Taverni, Gemma Easthope, Christopher Awai Skriabine, Sophie Corradi, Federico Longinotti, Luca Eng, Kynan Delbruck, Tobi Univ Zurich Inst Neuroinformat Zurich Switzerland Swiss Fed Inst Technol Inst Neuroinformat Zurich Switzerland Univ Zurich Balgrist Univ Hosp Zurich Switzerland IniVat AG Zurich Switzerland

ISBN: (纸本)9781728125060

Human pose estimation has dramatically improved thanks to the continuous developments in deep learning. However, marker-free human pose estimation based on standard frame-based cameras is still slow and power hungry for real-time feedback interaction because of the huge number of operations necessary for large Convolutional Neural Network (CNN) inference. Event-based cameras such as the Dynamic vision Sensor (DVS) quickly output sparse moving-edge information. Their sparse and rapid output is ideal for driving low-latency CNNs, thus potentially allowing real-time interaction for human pose estimators. Although the application of CNNs to standard frame based cameras for human pose estimation is well established, their application to event-based cameras is still under study. This paper proposes a novel benchmark dataset of human body movements, the Dynamic vision Sensor Human Pose dataset (DHP19). It consists of recordings from 4 synchronized 346x260 pixel DVS cameras, for a set of 33 movements with 17 subjects. DHP19 also includes a 3D pose estimation model that achieves an average 3D pose estimation error of about 8 cm, despite the sparse and reduced input data from the DVS.

关键词： Cameras

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Suppressing Model Overfitting for Image Super-Resolution Networks 32

Suppressing Model Overfitting for Image Super-Resolution Net...

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32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Feng, Ruicheng Gu, Jinjin Qiao, Yu Dong, Chao Chinese Acad Sci Shenzhen Inst Adv Technol ShenZhen Key Lab Comp Vis & Pattern Recognit SIAT SenseTime Joint Lab Beijing Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781728125060

Large deep networks have demonstrated competitive performance in single image super-resolution (SISR), with a huge volume of data involved. However, in real-world scenarios, due to the limited accessible training pairs, large models exhibit undesirable behaviors such as overfitting and memorization. To suppress model overfitting and further enjoy the merits of large model capacity, we thoroughly investigate generic approaches for supplying additional training data pairs. In particular, we introduce a simple learning principle MixUp to train networks on interpolations of sample pairs, which encourages networks to support linear behavior in-between training samples. In addition, we propose a data synthesis method with learned degradation, enabling models to use extra high-quality images with higher content diversity. This strategy proves to be successful in reducing biases of data. By combining these components MixUp and synthetic training data, large models can be trained without overfitting under very limited data samples and achieve satisfactory generalization performance. Our method won the second place in NTIRE2019 Real SR Challenge.

关键词： Optical resolving power

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MobileTouchDB: Mobile Touch Character Database in the Wild and Biometric Benchmark 32

MobileTouchDB: Mobile Touch Character Database in the Wild a...

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32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Tolosana, Ruben Gismero-Trujillo, Javier Vera-Rodriguez, Ruben Fierrez, Julian Ortega-Garcia, Javier Univ Autonoma Madrid Biometr & Data Pattern Analyt BiDA Lab Madrid Spain

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125060

In this paper;we introduce a new database of mobile touch on-line data named MobileTouchDB. The database contains more than 64K on-line character samples performed by 217 users, using 94 different smartphone models, with an average of 314 samples per user In each acquisition session, users had to draw all numbers (from 0 to 9), upper- and lower-case letters (54), different symbols (8), and passwords composed of 4 numbers (6). Regarding the acquisition protocol, MobileTouchDB comprises a maximum of 6 captured sessions per subject with a time gap between them of at least 2 days. This database studies an unsupervised mobile scenario with no restrictions in terms of position, posture, and devices. Users downloaded and used the acquisition app on their own devices freely. In addition, we also report a benchmark evaluation of biometric authentication on MobileTouchDB, providing an easily reproducible framework for two different scenarios of biometric user authentication: i) based on one character;and ii) based on character combinations. The database was collected with three main goals in mind: i) analyse the discriminative power of novel human touch interaction dynamics, ii) enhance traditional password authentication systems through the incorporation of touch. biometric information as a second level of user authentication, and iii) analyse the way we interact with mobile devices on a daily basis in order to enhance continuous authentication systems. MobileTouchDB is publicly, available in GitHub(1).

关键词： Databases Password Authentication Benchmark testing Protocols Mobile handsets Performance evaluation

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