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Three-Stream Convolutional Neural Network with Multi-task and Ensemble Learning for 3D Action recognition 32

Three-Stream Convolutional Neural Network with Multi-task an...

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

作者： Liang, Duohan Fan, Guoliang Lin, Guangfeng Chen, Wanjun Pan, Xiaorong Zhu, Hong Xian Univ Technol Xian 710048 Shaanxi Peoples R China Oklahoma State Univ Stillwater OK 74078 USA

ISBN: (纸本)9781728125060

In this paper, we propose a three-stream convolutional neural network (3SCNN) for action recognition from skeleton sequences, which aims to thoroughly and fully exploit the skeleton data by extracting, learning, fusing and inferring multiple motion-related features, including 3D joint positions andjoint displacements across adjacent frames as well as oriented bone segments. The proposed 3SCNN involves three sequential stages. The first stage enriches three independently extracted features by co-occurrence feature learning. The second stage involves multi-channel pairwise fusion to take advantage of the complementary and diverse nature among three features. The third stage is a multi-task and ensemble learning network to further improve the generalization ability of 3SCNN. Experimental results on the standard dataset show the effectiveness of our proposed multi-stream feature learning, fusion and inference method for skeleton-based 3D action recognition.

关键词： Convolution

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Color-Theoretic Experiments to Understand Unequal Gender Classification Accuracy from Face Images 32

Color-Theoretic Experiments to Understand Unequal Gender Cla...

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

作者： Muthukumar, Vidya Pedapati, Tejaswini Ratha, Nalini Sattigeri, Prasanna Wu, Chai-Wah Kingsbury, Brian Kumar, Abhishek Thomas, Samuel Mojsilovic, Aleksandra Varshney, Kush R. IBM Res Zurich Switzerland Univ Calif Berkeley Berkeley CA 94720 USA Google Brain Mountain View CA USA

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125060

Recent work shows unequal performance of commercial face classification services in the gender classification task across intersectional groups defined by skin type and gender. Accuracy on dark-skinned females is significantly worse than on any other group. We pro-vide initial evidence that skin type alone is not the driver for this disparity by conducting novel stability experiments that vary an image's skin type via color-theoretic methods, namely luminance mode-shift and optimal transport. We evaluate the effect of skin (rim change on the gender classification decision of a pair of state-of-the-art commercial and open-source gender classifiers. The results raise the possibility that broader *** in ethnicity, as opposed to the skin type alone, are what contribute to unequal gender classification accuracy in face images.

关键词： Skin Face Task analysis Image color analysis Benchmark testing computer vision Stability analysis

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Multiple People Tracking using Body and Joint Detections 32

Multiple People Tracking using Body and Joint Detections

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

作者： Henschel, Roberto Zou, Yunzhe Rosenhahn, Bodo Leibniz Univ Hannover Hannover Germany

ISBN: (纸本)9781728125060

Most multiple people tracking systems compute trajectories based on the tracking-by-detection paradigm. Consequently, the performance depends to a large extent on the quality of the employed input detections. However, despite an enormous progress in recent years, partially occluded people are still often not recognized. Also, many correct detections are mistakenly discarded when the non-maximum suppression is performed. Improving the tracking performance thus requires to augment the coarse input. Well suited for this task are fine-graded body joint detections, as they allow to locate even strongly occluded persons. Thus in this work, we analyze the suitability of including joint detections for multiple people tracking. We introduce different affinities between the two detection types and evaluate their performances. Tracking is then performed within a near-online framework based on a Min cost graph labeling formulation. As a result, our framework can recover heavily occluded persons and solve the data association efficiently. We evaluate our framework on the MOT16/17 benchmark. Experimental results demonstrate that our framework achieves state-of-the-art results.

关键词： computer vision

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Expression Classification in Children Using Mean Supervised Deep Boltzmann Machine 32

Expression Classification in Children Using Mean Supervised ...

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

作者： Nagpal, Shruti Singh, Maneet Vatsa, Mayank Singh, Richa Noore, Afzel IIIT Delhi Delhi India Texas A&M Univ Kingsville TX USA

ISBN: (纸本)9781728125060

Automated facial expression classification has widespread application in multiple domains such as human computer interaction, health and entertainment, biometrics, and security. There are six basic facial expressions: Anger, Disgust, Fear, Happiness, Sadness, and Surprise, apart from a neutral state. Most of the research in expression classification has focused on adult face images, with no dedicated research on automating expression classification for children. To the best of our knowledge, this is the first research which presents a deep learning based expression classification approach for children. A novel supervised deep learning formulation, termed as Mean Supervised Deep Boltzmann Machine (msDBM) is proposed which classifies an input face image into one of the seven expression classes. The proposed approach has been evaluated on two child face datasets - Radboud Faces and CAFE, along with experiments on the adult face images of the Radboud Faces dataset. Experimental results and analysis reinforces the challenging nature of the task at hand, and the effectiveness of the proposed msDBM model.

关键词： Human computer interaction

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LBVCNN: Local Binary Volume Convolutional Neural Network for Facial Expression recognition from Image Sequences 32

LBVCNN: Local Binary Volume Convolutional Neural Network for...

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

作者： Kumawat, Sudhakar Verma, Manisha Raman, Shanmuganathan Indian Inst Technol Gandhinagar Palaj Gujarat India Osaka Univ Suita Osaka Japan

ISBN: (纸本)9781728125060

Recognizing facial expressions is one of the central problems in computer vision. Temporal image sequences have useful spatio-temporalfeatures for recognizing expressions. In this paper, we propose a new 3D Convolution Neural Network (CNN) that can be trained end-to-end for facial expression recognition on temporal image sequences without using facial landmarks. More specifically, a novel 3D convolutional layer that we call Local Binary Volume (LBV) layer is proposed. The LBV layer, when used with our newly proposed LBVCNN network, achieve comparable results compared to state-of-the-art landmark-based or without landmark-based models on image sequences from CK+, Oulu-CASIA, and UNBC McMaster shoulder pain datasets. Furthermore, our LBV layer reduces the number of trainable parameters by a significant amount when compared to a conventional 3D convolutional layer. As a matter of fact, when compared to a 3 x 3 x 3 conventional 3D convolutional layer, the LBV layer uses 27 times less trainable parameters.

关键词： Convolution

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Pose-Guided R-CNN for Jersey Number recognition in Sports 32

Pose-Guided R-CNN for Jersey Number Recognition in Sports

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

作者： Liu, Hengyue Bhanu, Bir Univ Calif Riverside Ctr Res Intelligent Syst Riverside CA 92521 USA

ISBN: (纸本)9781728125060

Recognizing player jersey number in sports match video streams is a challenging computer vision task. The human pose and view-point variations displayed in frames lead to many difficulties in recognizing the digits on jerseys. These challenges are addressed here using an approach that exploits human body part cues with a Region based Convolutional Neural Network (R-CNN) variant for digit level localization and classification. The paper first adopts the Region Proposal Network (RPN) to perform anchor classification and bounding-box regression over three classes: background, person and digit. The person and digit proposals are geometrically related and fed to a network classifier. Subsequently, it introduces a human body key-point prediction branch and a pose-guided regressor to get better bounding-box offsets for generating digit proposals. A novel dataset of soccer-match video frames with corresponding multi-digit class labels, player and jersey number bounding boxes, and single digit segmentation masks is collected. Our framework outperforms all existing models on jersey number recognition task. This work will be essential to the automation of player identification across multiple sports, and releasing the dataset will ease future research on sports video analysis.

关键词： Sports

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The Attack Generator: A Systematic Approach Towards Constructing Adversarial Attacks 32

The Attack Generator: A Systematic Approach Towards Construc...

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

作者： Assion, Felix Schlicht, Peter Gressner, Florens Gunther, Wiebke Huger, Fabian Schmidt, Nico Rasheed, Umair Neurocat GmbH Berlin Germany Volkswagen AG Wolfsburg Germany

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125060

Most state-of-the-art machine learning (ML) classification systems are vulnerable to adversarial perturbations. As a consequence, adversarial robustness poses a significant challenge for the deployment of ML-based systems in safety- and security-critical environments like autonomous driving, disease detection or unmanned aerial vehicles. In the past years we have seen an impressive amount of publications presenting more and more new adversarial attacks. However, the attack research seems to be rather unstructured and new attacks often appear to be random selections from the unlimited set of possible adversarial attacks. With this publication, we present a structured analysis of the adversarial attack creation process. By detecting different building blocks of adversarial attacks, we outline the road to new sets of adversarial attacks. We call this the "attack generator". In the pursuit of this objective, we summarize and extend existing adversarial perturbation taxonomies. The resulting taxonomy is then linked to the application context of computer vision systems for autonomous vehicles, i.e. semantic segmentation and object detection. Finally, in order to prove the usefulness of the attack generator, we investigate existing semantic segmentation attacks with respect to the detected defining components of adversarial attacks.

关键词： Perturbation methods Taxonomy Robustness Semantics Task analysis computer vision Safety

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Exploring Factors for Improving Low Resolution Face recognition 32

Exploring Factors for Improving Low Resolution Face Recognit...

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

作者： Aghdam, Omid Abdollahi Bozorgtabar, Behzad Ekenel, Hazim Kemal Thiran, Jean-Philippe ITU SiMiT Lab Istanbul Turkey Ecole Polytech Fed Lausanne LTS5 Lausanne Switzerland

ISBN: (纸本)9781728125060

State-of-the-art deep face recognition approaches report near perfect performance on popular benchmarks, e.g., Labeled Faces in the Wild. However, their performance deteriorates significantly when they are applied on low quality images, such as those acquired by surveillance cameras. A further challenge for low resolution face recognition for surveillance applications is the matching of recorded low resolution probe face images with high resolution reference images, which could be the case in watchlist scenarios. In this paper, we have addressed these problems and investigated the factors that would contribute to the identification performance of the state-of-the-art deep face recognition models when they are applied to low resolution face recognition under mismatched conditions. We have observed that the following factors affect performance in a positive way: appearance variety and resolution distribution of the training dataset, resolution matching between the gallery and probe images, and the amount of information included in the probe images. By leveraging this information, we have utilized deep face models trained on MS-Celeb-IM and fine-tuned on VGGFace2 dataset and achieved state-of-the-art accuracies on the SCFace and ICB-RW benchmarks, even without using any training data from the datasets of these benchmarks.

关键词： Face recognition

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Live Demonstration: Digit recognition On Pixel Processor Arrays 32

Live Demonstration: Digit Recognition On Pixel Processor Arr...

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

作者： Bose, Laurie Chen, Jianing Carey, Stephen J. Dudek, Piotr Mayol-Cuevas, Walterio Univ Bristol Fac Engn Comp Sci Bristol Avon England Univ Manchester Sch Elect & Elect Engn Manchester Lancs England

ISBN: (纸本)9781728125060

In this demo, we will showcase recent work on implementing convolutional neural networks directly on pixel processor arrays (PPA). As CNNs demonstrate enhanced performance across tasks from classification to image synthesis, it becomes essential to find the most adequate ways to realize them, especially for embedded, real-time and reactive tasks in areas across computer vision and Robotics. The PPA concept is one architecture that pairs sensing and massively parallel processing at the focal plane level and allow mid to high level tasks to be run wholly embedded within them. They allow operation at high framerates and low energy consumption (<= 2W), and without the need for external signal interpretation or processing. In this demo we will showcase our recent work on the implementation of CNNs on the SCAMPS architecture I I as a step towards true end-to-end operation on flexibly programmable PPA hardware. In particular, we will showcase handwritten number classification from image capture to classification wholly embedded on the PPA.

关键词： Energy utilization

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APA: Adaptive Pose Alignment for Robust Face recognition 32

APA: Adaptive Pose Alignment for Robust Face Recognition

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

作者： An, Zhanfu Deng, Weihong Zhong, Yaoyao Huang, Yaohai Tao, Xunqiang Beijing Univ Posts & Telecommun Beijing Peoples R China Canon Informat Technol Beijing Co Ltd Beijing Peoples R China

ISBN: (纸本)9781728125060

In this paper, we propose a new face alignment method, called adaptive pose alignment (APA) which can greatly reduce the intra-class difference and correct the noise caused by the traditional method in the alignment process, especially in unconstrained settings. Instead of aligning all faces to the pre-defined, uniform frontal shape, we adaptively learn the alignment templates according to the facial poses and then align each face of training or testing sets to its related template. To further improve the face recognition performance, we propose a simple, yet effective feature normalization method which can generate more discriminative feature representation of a face or template combined with the APA method. Furthermore, we introduce a pose invariant face recognition pipeline that sequentially applies APA based alignment, deep representation by Softmax or Arcface, and the effective feature normalization procedure. We empirically show that APA based images can accelerate the training of deep face recognition model by aligning all the images to the optimal templates. Moreover, experiments show that the proposed method achieves the state-of-the-art performance on challenging IJB-A, IJB-C and CPLFW datasets.

关键词： Alignment

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