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WiCV 2019: The Sixth Women In computer vision Workshop

WiCV 2019: The Sixth Women In Computer Vision Workshop

引用

ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Irene Amerini Elena Balashova Sayna Ebrahimi Kathryn Leonard Arsha Nagrani Amaia Salvador University of Florence Princeton University UC Berkeley Occidental College University of Oxford Universitat Politcnica de Catalunya

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

In this paper we present the Women in computer vision Workshop - WiCV 2019, organized in conjunction with cvpr 2019. This event is meant for increasing the visibility and inclusion of women researchers in computer vision field. computer vision and machine learning have made incredible progress over the past years, but the number of female researchers is still low both in the academia and in the industry. WiCV is organized especially for this reason: to raise visibility of female researchers, to increase collaborations between them, and to provide mentorship to female junior researchers in the field. In this paper, we present a report of trends over the past years, along with a summary of statistics regarding presenters, attendees, and sponsorship for the current workshop.

关键词： conferences computer vision Machine learning Engineering profession Facebook Buildings

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A Comparative Study of Real-time Semantic Segmentation for Autonomous Driving 31

A Comparative Study of Real-time Semantic Segmentation for A...

引用

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

作者： Siam, Mennatullah Gamal, Mostafa Abdel-Razek, Moemen Yogamani, Senthil Jagersand, Martin Zhang, Hong Univ Alberta Edmonton AB Canada Cairo Univ Giza Egypt Valeo Vis Syst Tuam Ireland

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Semantic segmentation is a critical module in robotics related applications, especially autonomous driving. Most of the research on semantic segmentation is focused on improving the accuracy with less attention paid to computationally efficient solutions. Majority of the efficient semantic segmentation algorithms have customized optimizations without scalability and there is no systematic way to compare them. In this paper, we present a real-time segmentation benchmarking framework and study various segmentation algorithms for autonomous driving. We implemented a generic meta-architecture via a decoupled design where different types of encoders and decoders can be plugged in independently. We provide several example encoders including VGG16, Resnet18, MobileNet, and ShuffleNet and decoders including SkipNet, UNet and Dilation Frontend. The framework is scalable for addition of new encoders and decoders developed in the community for other vision tasks. We performed detailed experimental analysis on cityscapes dataset for various combinations of encoder and decoder. The modular framework enabled rapid prototyping of a custom efficient architecture which provides similar to x143 GFLOPs reduction compared to SegNet and runs real-time at similar to 15 fps on NVIDIA Jetson TX2. The source code of the framework is publicly available(1).

关键词： Convolution Semantics computer architecture Decoding Context modeling Real-time systems Image segmentation

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Empirically Analyzing the Effect of Dataset Biases on Deep Face recognition Systems 31

Empirically Analyzing the Effect of Dataset Biases on Deep F...

引用

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: (数字)9781538661000

ISBN: (纸本)9781538661000

It is unknown what kind of biases modern in the wild face datasets have because of their lack of annotation. A direct consequence of this is that total recognition rates alone only provide limited insight about the generalization ability of a Deep Convolutional Neural Networks (DCNNs). We propose to empirically study the effect of different types of dataset biases on the generalization ability of DCNNs. Using synthetically generated face images, we study the face recognition rate as a function of interpretable parameters such as face pose and light. The proposed method allows valuable details about the generalization performance of different DCNN architectures to be observed and compared. In our experiments, we find that: 1) Indeed, dataset bias has a significant influence on the generalization performance of DCNNs. 2) DCNNs can generalize surprisingly well to unseen illumination conditions and large sampling gaps in the pose variation. 3) Using the presented methodology we reveal that the VGG-16 architecture outperforms the AlexNet architecture at face recognition tasks because it can much better generalize to unseen face poses, although it has significantly more parameters. 4) We uncover a main limitation of current DCNN architectures, which is the difficulty to generalize when different identities to not share the same pose variation. 5) We demonstrate that our findings on synthetic data also apply when learning from real-world data. Our face image generator is publicly available to enable the community to benchmark other DCNN architectures.

关键词： Face Face recognition Lighting Generators Training data computer architecture Task analysis

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Predicting Dynamical Evolution of Human Activities from a Single Image 31

Predicting Dynamical Evolution of Human Activities from a Si...

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

作者： Lohit, Suhas Bansal, Ankan Shroff, Nitesh Pillai, Jaishanker Turaga, Pavan Chellappa, Rama Arizona State Univ Geometr Media Lab Tempe AZ 85281 USA Univ Maryland Ctr Automat Res UMIACS College Pk MD 20742 USA Zoox Inc Foster City CA USA Google Res Mountain View CA USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

A human pose often conveys not only the configuration of the body parts, but also implicit predictive information about the ensuing motion. This dynamic information can benefit vision applications which lack explicit motion cues. The human visual system can easily perceive the dynamic information in still images. However, computational algorithms to infer and utilize it in computer vision applications are limited. In this paper, we propose a probabilistic framework to infer the dynamic information associated with a human pose. The inference problem is posed as a non-parametric density estimation problem on a non-Euclidean manifold of linear dynamical models. Since direct modeling is intractable, we develop a data driven approach, estimating the density for the test sample under consideration. Statistical inference on the estimated density provides us with quantities of interest like the most probable future motion of the human and the amount of motion information conveyed by a pose. Our experiments demonstrate that the extracted motion information benefits numerous applications in computer vision. In particular, the predicted future motion is useful for activity recognition, human trajectory synthesis, and motion prediction.

关键词： Dynamics Motion segmentation Trajectory Uncertainty Training Activity recognition Computational modeling

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Assessing Shape Bias Property of Convolutional Neural Networks 31

Assessing Shape Bias Property of Convolutional Neural Networ...

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

作者： Hosseini, Hossein Xiao, Baicen Jaiswal, Mayoore Poovendran, Radha Univ Washington Dept Elect Engn NSL Seattle WA 98195 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

It is known that humans display "shape bias" when classifying new items, i.e., they prefer to categorize objects based on their shape rather than color. Convolutional Neural Networks (CNNs) are also designed to take into account the spatial structure of image data. In fact, experiments on image datasets, consisting of triples of a probe image, a shape-match and a color-match, have shown that one-shot learning models display shape bias as well. In this paper, we examine the shape bias property of CNNs. In order to conduct large scale experiments, we propose using the model accuracy on images with reversed brightness as a metric to evaluate the shape bias property. Such images, called negative images, contain objects that have the same shape as original images, but with different colors. Through extensive systematic experiments, we investigate the role of Afferent factors, such as training data, model architecture, initialization and regularization techniques, on the shape bias property of CNNs. We show that it is possible to design different CNNs that achieve similar accuracy on original images, but perform significantly different on negative images, suggesting that CNNs do not intrinsically display shape bias. We then show that CNNs are able to learn and generalize the structures, when the model is properly initialized or data is properly augmented, and if batch normalization is used.

关键词： computer vision pattern recognition conferences

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Toward More Realistic Face recognition Evaluation Protocols for the YouTube Faces Database 31

Toward More Realistic Face Recognition Evaluation Protocols ...

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

作者： Martinez-Diaz, Yoanna Mendez-Vazquez, Heydi Lopez-Avila, Leyanis Chang, Leonardo Sucar, L. Enrique Tistarelli, Massimo Adv Technol Applicat Ctr CENATAV 7A 21406 Siboney Havana 12200 Cuba Tecnol Monterrey Ciudad Lopez Mateos Estado De Mexic Mexico INAOE Puebla Mexico Univ Sassari Sassari Italy

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

One of the key factors to measure the progress of a research problem is the design of appropriate evaluation protocols defined on suitable databases. Recently, the introduction of comprehensive databases and benchmarks of face videos has had a great impact on the development of new face recognition techniques. However, most of the protocols provided for these datasets are limited and do not capture requirements of unconstrained scenarios. That is why sometimes the performance of face recognition methods on current benchmarks seems to be saturated. To address this lack, the tendency is to collect new datasets, which is more expensive and sometimes the main the problem is not the data but the protocols. In this work, we propose new relevant evaluation protocols for the YouTube Faces database (REP-YTF) supporting face verification and open/ closed-set identification. The proposal better fits realistic face recognition scenarios and allows us to test existing algorithms at relevant assessment points, under different openness values and taking both videos and images as the gallery. We provide an extensive experimental evaluation, by combining several well-established feature representations with three different metric learning algorithms. The obtained results show that by using the proposed evaluation protocols, there is room for improvement in the recognition performance on the YouTube Faces database.

关键词： Videos Face Protocols Databases Face recognition Measurement Standards

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FACSCaps: Pose-Independent Facial Action Coding with Capsules 31

FACSCaps: Pose-Independent Facial Action Coding with Capsule...

引用

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

作者： Ertugrul, Itir Onal Jeni, Laszlo A. Cohn, Jeffrey F. Carnegie Mellon Univ Robot Inst Pittsburgh PA 15213 USA Univ Pittsburgh Dept Psychol Pittsburgh PA 15260 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Most automated facial expression analysis methods treat the face as a 2D object, flat like a sheet of paper. That works well provided images are frontal or nearly so. In real-world conditions, moderate to large head rotation is common and system performance to recognize expression degrades. Multi-view Convolutional Neural Networks (CNNs) have been proposed to increase robustness to pose, but they require greater model sizes and may generalize poorly across views that are not included in the training set. We propose FACSCaps architecture to handle multi-view and multi-label facial action unit (AU) detection within a single model that can generalize to novel views. Additionally, FACSCaps's ability to synthesize faces enables insights into what is leaned by the model. FACSCaps models video frames using matrix capsules, where hierarchical pose relationships between face parts are built into internal representations. The model is trained by jointly optimizing a multi-label loss and the reconstruction accuracy. FACSCaps was evaluated using the FERA 2017 facial expression dataset that includes spontaneous facial expressions in a wide range of head orientations. FACSCaps outperformed both state-of-the-art CNNs and their temporal extensions.

关键词： Gold Image reconstruction computer architecture Face Training Feature extraction Routing

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SqueezeNext: Hardware-Aware Neural Network Design 31

SqueezeNext: Hardware-Aware Neural Network Design

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

作者： Gholami, Amir Kwon, Kiseok Wu, Bichen Tai, Zizheng Yue, Xiangyu Jin, Peter Zhao, Sicheng Keutzer, Kurt Univ Calif Berkeley EECS Berkeley CA 94720 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

One of the main barriers for deploying neural networks on embedded systems has been large memory and power consumption of existing neural networks. In this work, we introduce SqueezeNext, a new family of neural network architectures whose design was guided by considering previous architectures such as SqueezeNet, as well as by simulation results on a neural network accelerator. This new network is able to match AlexNet's accuracy on the ImageNet benchmark with 112x fewer parameters, and one of its deeper variants is able to achieve VGG-19 accuracy with only 4.4 Million parameters, (31x smaller than VGG19). SqueezeNext also achieves better top-5 classification accuracy with 1.3x fewer parameters as compared to MobileNet, but avoids using depthwise-separable convolutions that are inefficient on some mobile processor platforms. This wide range of accuracy gives the user the ability to make speed-accuracy tradeoffs, depending on the available resources on the target hardware. Using hardware simulation results for power and inference speed on an embedded system has guided us to design variations of the baseline model that are 2.59x/8.26x faster and 2.25x/7.5x more energy efficient as compared to SqueezeNet/AlexNet without any accuracy degradation.

关键词： Neural networks Hardware computer architecture Semiconductor process modeling Embedded systems Power demand Computational modeling

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Design of a Reconfigurable 3D Pixel-Parallel Neuromorphic Architecture for Smart Image Sensor 31

Design of a Reconfigurable 3D Pixel-Parallel Neuromorphic Ar...

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

作者： Bhowmik, Pankaj Pantho, Jubaer Hossain Asadinia, Marjan Bobda, Christophe Univ Arkansas Fayetteville AR 72701 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Power reduction and speed-up of image processing algorithms remain of high interest as image resolutions continue to increase. Neuromorphic-circuits are inspired by the nervous system aiming to reduce power consumption and speed-up. This paper presents a neuromorphic smart image sensor designed by the pixel-parallel 3D hierarchical architecture with an on-chip attention module. The module dynamically detects regions with relevant information and produces a feedback path to sample those regions at high speed. On the other hand, by sampling non-relevant regions with a low-speed, the sensor can reduce redundancy and enable high-performance computing by ensuring low-power operation. The image sensor is comprised of several hierarchical planes and each plane has small and independent reconfigurable computational units (XPU). In each plane, all XPUs operate in parallel with a different operating speed which gives a pixel-parallel architecture. When the raw image passes through the hierarchical planes, necessary image processing algorithms are performed in parallel on different planes at a variable clock rate for saving power and reducing redundancy. The goal of this work is to prototype the focal plane image sensor which emulates the brain features. The results show that the prototype achieves remarkable power saving and speed-up at different stages.

关键词： computer architecture Image sensors Visualization Program processors Clocks Three-dimensional displays Image processing

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Merging Deep Neural Networks for Mobile Devices 31

Merging Deep Neural Networks for Mobile Devices

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

作者： Chou, Yi-Min Chan, Yi-Ming Lee, Jia-Hong Chiu, Chih-Yi Chen, Chu-Song Acad Sinica Inst Informat Sci Taipei Taiwan MOST Joint Res Ctr AI Technol & Vista Healthcare Taipei Taiwan Natl Chiayi Univ 300 Syuefu Rd Chiayi Taiwan

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In this paper, a novel method to merge convolutional neural networks for the inference stage is introduced. When two feed-forward networks already trained for handling different tasks are given, our method can align the layers of these networks and merge them into a unified model by sharing the representative weights. The performance of the merged model can be restored or improved via re-training. Without needing high-performance hardware, the proposed method effectively produces a compact model to run the original tasks simultaneously on resource-limited devices. The system development time, as well as training overhead, is substantially reduced because our method leverages the co-used weights and preserves the general architectures of the well-trained networks. The merged model is jointly compressed and can be implemented faster than the original models with a comparable accuracy. When combining VGG-Avg and ZF-Net models, our approach can achieve higher than 12 and 2.5 times of compression and speedup ratios compared to the original whole models, respectively, while the accuracy remains approximately the same.

关键词： Task analysis Convolution Merging Computational modeling Neural networks Kernel computer architecture

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