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Multimodal 2D and 3D for In-the-wild Facial Expression recognition 32

Multimodal 2D and 3D for In-the-wild Facial Expression Recog...

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

作者： Ly, Son Thai Nhu-Tai Do Lee, Guee-Sang Kim, Soo-Hyung Yang, Hyung-Jeong Chonnam Natl Univ Dept Elect & Comp Engn Gwangju South Korea

ISBN: (纸本)9781728125060

In this paper, unlike other in-the-wild facial expression recognition (FER) studies which only focused on 2D information, we present a fusion approach for 2D and 3D facial data in FER. In particular, the 3D facial data are first reconstructed from image datasets. The 3D information are then extracted by deep learning technique that could exploit the meaningful facial geometry details for expression. We further demonstrate the potential of using 3D facial data by taking the 2D projected images of 3D face as an additional input for FER. These features are fused with that of 2D features from a typical network. Following the experiment procedure in recent studies, the concatenated features are classified by linear support vector machines (SVMs). Comprehensive experiments are further conducted on integrating facial features for expression prediction. The results show that the proposed method achieves state-of-the-art recognition performances on both RAF database and SFEW 2.0 database. This is the first time such a deep learning combination of 3D and 2D facial modalities is presented in the context of in-the-wild FER.

关键词： Deep learning

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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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Distilled Person Re-identification: Towards a More Scalable System 32

Distilled Person Re-identification: Towards a More Scalable ...

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

作者： Wu, Ancong Zheng, Wei-Shi Guo, Xiaowei Lai, Jian-Huang Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou Guangdong Peoples R China Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Guangdong Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou Guangdong Peoples R China Guangdong Prov Key Lab Informat Secur Guangzhou Guangdong Peoples R China Tencent YouTu Lab Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781728132938

Person re-identification (Re-ID), for matching pedestrians across non-overlapping camera views, has made great progress in supervised learning with abundant labelled data. However, the scalability problem is the bottleneck for applications in large-scale systems. We consider the scalability problem of Re-ID from three aspects: (1) low labelling cost by reducing label amount, (2) low extension cost by reusing existing knowledge and (3) low testing computation cost by using lightweight models. The requirements render scalable Re-ID a challenging problem. To solve these problems in a unified system, we propose a Multi-teacher Adaptive Similarity Distillation Framework, which requires only a few labelled identities of target domain to transfer knowledge from multiple teacher models to a user-specified lightweight student model without accessing source domain data. We propose the Log-Euclidean Similarity Distillation Loss for Re-ID and further integrate the Adaptive Knowledge Aggregator to select effective teacher models to transfer target-adaptive knowledge. Extensive evaluations show that our method can extend with high scalability and the performance is comparable to the state-of-the-art unsupervised and semi-supervised Re-ID methods.

关键词： And Body Pose Categorization Face Gesture recognition: Detection Retrieval vision Applications and Systems

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Analyzing and Reducing the Damage of Dataset Bias to Face recognition with Synthetic Data 32

Analyzing and Reducing the Damage of Dataset Bias to Face Re...

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

作者： Kortylewski, Adam Egger, Bernhard Schneider, Andreas Gerig, Thomas Morel-Forster, Andreas Vetter, Thomas Univ Basel Dept Math & Comp Sci Basel Switzerland

ISBN: (纸本)9781728125060

It is well known that deep learning approaches to face recognition sufferfrom various biases in the available training data. In this work, we demonstrate the large potential of synthetic data for analyzing and reducing the negative effects of dataset bias on deep face recognition systems. In particular we explore two complementary application areas for synthetic face images: 1) Using filly annotated synthetic face images we can study the face recognition rate as a function of interpretable parameters such as face pose. This enables us to systematically analyze the effect of different types of dataset biases on the generalization ability of neural network architectures. Our analysis reveals that deeper neural network architectures can generalize better to unseen face poses. Furthermore, our study shows that current neural network architectures cannot disentangle face pose and facial identity, which limits their generalization ability. 2) We pre -train neural networks with large-scale synthetic data that is highly variable in face pose and the number of facial identities. After a subsequent fine-tuning with realworld data, we observe that the damage of dataset bias in the real-world data is largely reduced. Furthermore, we demonstrate that the size of real-world datasets can be reduced by 75% while maintaining competitive face recognition performance. The data and software used in this work are publicly available(1).

关键词： Face recognition

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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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DupNet: Towards Very Tiny Quantized CNN with Improved Accuracy for Face Detection 32

DupNet: Towards Very Tiny Quantized CNN with Improved Accura...

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

作者： Gao, Hongxing Tao, Wei Wen, Dongchao Liu, Junjie Chen, Tse-Wei Osa, Kinya Kato, Masami Canon Informat Technol Beijing Co LTD Beijing Peoples R China Canon Inc Device Technol Dev Headquarters Beijing Peoples R China

ISBN: (纸本)9781728125060

Deploying deep learning based face detectors on edge devices is a challenging task due to the limited computation resources. Even though binarizing the weights of a very tiny network gives impressive compactness on model size (e.g. 240.9 KB for IFQ-Tinier-YOLO), it is not tiny enough to fit in the embedded devices with strict memory constraints. In this paper, we propose DupNet which consists of two parts. Firstly, we employ weights with duplicated channels for the weight-intensive layers to reduce the model size. Secondly, for the quantization -sensitive layers whose quantization causes notable accuracy drop, we duplicate its inputfeature maps. It allows us to use more weights channels for convolving more representative outputs. Based on that, we propose a very tiny face detector, DupNet-Tinier-YOLO, which is 6.5x times smaller on model size and 42.0% less complex on computation and meanwhile achieves 2.4% higher detection than IFQ-Tinier-YOLO. Comparing with the full precision Tiny-YOLO, our DupNet-Tinier-YOLO gives 1,694.2x and 389.9x times savings on model size and computation complexity respectively with only 4.0% drop on detection rate (0.880 vs. 0.920). Moreover, our DupNet-Tinier-YOLO is only 36.9 KB, which is the tiniest deep face detector to our best knowledge.

关键词： Face recognition

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A Hybrid Method for Tracking of Objects by UAVs 32

A Hybrid Method for Tracking of Objects by UAVs

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

作者： Saribas, Hasan Uzun, Bedirhan Benligiray, Burak Eker, Onur Cevikalp, Hakan Eskisehir Tech Univ 2 Eylul Kampusu TR-26470 Eskisehir Turkey Eskisehir Osmangazi Univ Meselik Kampusu TR-26480 Eskisehir Turkey

ISBN: (纸本)9781728125060

Object tracking remains one of the fundamental problems of computer vision since it becomes difficult under some realistic conditions such as fast camera movement, occlusion and similar of objects to the tracked target. As a real-world application, tracking objects using cameras mounted on unmanned aerial vehicles (UAVs) has become very popular. With the increasing availability of small single board computers with high parallel processing power capabilities, tracking of objects by using onboard computers within UAVs in real time has become feasible. Although these onboard computers allow a wide variety of computer vision methods to be executed on a UAV, there is still a need to optimize these methods for running time and power consumption. In this paper, we propose a hybrid method for a UAV to detect and track other UAVs efficiently. To detect the target UAV at the beginning of the video and in the case where the tracked UAV has been lost, we use the deep learning-based YOLOv3 and YOLOv3-Tiny models, which provide one of the best trade-offs between speed and accuracy in the literature. To track the detected UAVs in real time, a kernelized correlation filter is used. Combining these two methods provides high accuracy and speed even on onboard computers. To train the neural nets and test our method, we have collected a new dataset composed of videos of various UAVs in flight, captured from another UAV. The performance of the proposed method has been compared with other state-of-the-art methods in the literature on this dataset. Additionally, we also tested the proposed trackers on aerial videos captured from UAVs. Experimental results show that the proposed hybrid trackers achieve the state-of-the-art performance on all tested datasets. The code is available at https://***/bdrhn9/hybrid-tracker.

关键词： computer vision

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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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Proceedings - 2019 ieee/CVF conference on computer vision and pattern recognition, cvpr 2019

Proceedings - 2019 IEEE/CVF Conference on Computer Vision an...

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32nd ieee/CVF conference on computer vision and pattern recognition, cvpr 2019

ISBN: (纸本)9781728132938

The proceedings contain 1294 papers. The topics discussed include: finding task-relevant features for few-shot learning by category traversal;edge-labeling graph neural network for few-shot learning;generating classification weights with GNN denoising autoencoders for few-shot learning;kervolutional neural networks;why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem;on the structural sensitivity of deep convolutional networks to the directions of fourier basis functions;hardness-aware deep metric learning;auto-deeplab: hierarchical neural architecture search for semantic image segmentation;striking the right balance with uncertainty;and SDRSAC: semidefinite-based randomized approach for robust point cloud registration without correspondences.

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