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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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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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Hybrid User-Independent and User-Dependent Offline Signature Verification with a Two-Channel CNN 31

Hybrid User-Independent and User-Dependent Offline Signature...

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

作者： Yilmaz, Mustafa Berkay Ozturk, Kagan Akdeniz Univ Antalya Turkey

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Signature verification task needs relevant signature representations to achieve low error rates. Many signature representations have been proposed so far. In this work we propose a hybrid user-independent/dependent offline signature verification technique with a two-channel convolutional neural network (CNN) both for verification and feature extraction. Signature pairs are input to the CNN as two channels of one image, where the first channel always represents a reference signature and the second channel represents a query signature. We decrease the image size through the network by keeping the convolution stride parameter large enough. Global average pooling is applied to decrease the dimensionality to 200 at the end of locally connected layers. We utilize the CNN as a feature extractor and report 4.13% equal error rate (EER) considering 12 reference signatures with the proposed 200-dimensional representation, compared to 3.66% of a recently proposed technique with 2048-dimensional representation using the same experimental protocol. When the two methods are combined at score level, more than 50% improvement (1.76% EER) is achieved demonstrating the complementarity of them. Sensitivity of the model to gray-level and binary images is investigated in detail. One model is trained using gray-level images and the other is trained using binary images. It is shown that the availability of gray-level information in train and test data decreases the EER e.g. from 11.86% to 4.13%.

关键词： Feature extraction Forgery Training Error analysis Databases Measurement computer architecture

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From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation 31

From apparent to real age: gender, age, ethnic, makeup, and ...

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

作者： Clapes, Albert Anbarjafari, Gholamreza Bilici, Ozan Temirova, Dariia Avots, Egils Escalera, Sergio Comp Vis Ctr Barcelona Spain Univ Barcelona Barcelona Spain Univ Tartu iCV Lab Tartu Estonia GoSwift Inc Tartu Estonia Hasan Kalyoncu Univ Gaziantep Turkey

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Real age estimation in still images of faces is an active area of research in the computer vision community. However, very few works attempted to analyse the apparent age as perceived by observers. Apparent age estimation is a subjective task, which is affected by many factors present in the image as well as by observer's characteristics. In this work, we enhance the APPA-REAL dataset, containing around 8K images with real and apparent ages, with new annotated attributes, namely gender, ethnic, makeup, and expression. Age and gender from a subset of guessers is also provided. We show there exists some consistent bias for a subset of these attributes when relating apparent to real age. In addition we run simple experiments with a basic Convolutional Neural Network (CNN) showing that considering apparent labels for training improves real age estimation rather than training with real ages. We also perform bias correction on CNN predictions, showing that it further enhance final age recognition performance.

关键词： Estimation computer vision Databases Visualization Training Aging Support vector machines

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Deep-BCN: Deep networks meet biased competition to create a brain-inspired model of attention control 31

Deep-BCN: Deep networks meet biased competition to create a ...

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

作者： Adeli, Hossein Zelinsky, Gregory SUNY Stony Brook Stony Brook NY 11794 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

The mechanism of attention control is best described by biased-competition theory (BCT), which suggests that a top-down goal state biases a competition among object representations for the selective routing of a visual input for classification. Our work advances this theory by making it computationally explicit as a deep neural network (DNN) model, thereby enabling predictions of goal-directed attention control using real-world stimuli. This model, which we call Deep-BCN, is built on top of an 8-layer DNN pre-trained for object classification, but has layers mapped to early visual (V1, V2/V3, V4), ventral (PIT, AIT), and frontal (PFC) brain areas that have their functional connectivity informed by BCT. Deep-BCN also has a superior colliculus and a frontal-eye field, and can therefore make eye movements. We compared Deep-BCN's eye movements to those made from 15 people performing a categorical search for one of 25 target object categories, and found that it predicted both the number of fixations during search and the saccade-distance travelled before search termination. With Deep-BCN a DNN implementation of BCT now exists, which can be used to predict the neural and behavioral responses of an attention control mechanism as it mediates a goal-directed behavior-in our study the eye movements made in search of a target goal.

关键词： Visualization Brain modeling Computational modeling Object detection Search problems computer vision

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Towards dense object tracking in a 2D honeybee hive 31

Towards dense object tracking in a 2D honeybee hive

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31st ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Bozek, Katarzyna Hebert, Laetitia Mikheyev, Alexander S. Stephens, Greg J. OIST Grad Univ Okinawa Japan Vrije Univ Amsterdam Fac Sci Amsterdam Netherlands

ISBN: (纸本)9781538664209

From human crowds to cells in tissue, the detection and efficient tracking of multiple objects in dense configurations is an important and unsolved problem. In the past, limitations of image analysis have restricted studies of dense groups to tracking a single or subset of marked individuals, or to coarse-grained group-level dynamics, all of which yield incomplete information. Here, we combine convolutional neural networks (CNNs) with the model environment of a honeybee hive to automatically recognize all individuals in a dense group from raw image data. We create new, adapted individual labeling and use the segmentation architecture U-Net with a loss function dependent on both object identity and orientation. We additionally exploit temporal regularities of the video recording in a recurrent manner and achieve near human-level performance while reducing the network size by 94% compared to the original U-Net architecture. Given our novel application of CNNs, we generate extensive problem-specific image data in which labeled examples are produced through a custom interface with Amazon Mechanical Turk. This dataset contains over 375,000 labeled bee instances across 720 video frames at 2 FPS, representing an extensive resource for the development and testing of tracking methods. We correctly detect 96% of individuals with a location error of similar to 7% of a typical body dimension, and orientation error of 12, approximating the variability of human raters. Our results provide an important step towards efficient image-based dense object tracking by allowing for the accurate determination of object location and orientation across time-series image data efficiently within one network architecture.

关键词： Labeling Video recording Object tracking Image recognition Image resolution Light emitting diodes

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