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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...

引用

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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Merging Deep Neural Networks for Mobile Devices 31

Merging Deep Neural Networks for Mobile Devices

引用

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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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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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 ...

引用

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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Unsupervised Person Re-Identification with Iterative Self-Supervised Domain Adaptation

Unsupervised Person Re-Identification with Iterative Self-Su...

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： Haotian Tang Yiru Zhao Hongtao Lu Key Lab of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

In real applications, person re-identification (re-id) is an inherently domain adaptive computer vision task which often requires the model trained on a group of people to perform well on an unlabeled dataset consisting of another group of pedestrians without supervised fine-tuning. Furthermore, there are typically a large number of classes (people) with small number of samples belonging to each class. Based on the characteristics of person re-id and general assumptions related to domain adaptation, we put forward a novel algorithm for cross-dataset person re-id. Our idea is simple yet effective: first, we preprocess the source dataset with style transfer GAN and train a baseline on it in a supervised learning manner, then we assign pseudo labels to unlabeled samples in target dataset based on the model trained on labeled source dataset;finally, we train on the target dataset with pseudo labels in traditional supervised learning manner. We adopt the idea of co-training in the training process to make the pseudo labels more reliable. We show the superiority of our model over all state-of-the-art methods through extensive experiments.

关键词： Persons Iterative Dataset Pseudo Semi-supervised Learning Domain Pedestrians

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

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

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ieee/cvf conference on computer vision and pattern recognition workshops

作者： Son Thai Ly Nhu-Tai Do Guee-Sang Lee Soo-Hyung Kim Hyung-Jeong Yang Department of Electronics and Computer Engineering Chonnam National University

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

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： FER Gene Iron Face Three Dimensions expression recognition

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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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On the Suitability of L_p-norms for Creating and Preventing Adversarial Examples 31

On the Suitability of <i>L<sub>p</sub></i>-norms for Creatin...

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

作者： Sharif, Mahmood Bauer, Lujo Reiter, Michael K. Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ N Carolina Chapel Hill NC 27515 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Much research has been devoted to better understanding adversarial examples, which are specially crafted inputs to machine-learning models that are perceptually similar to benign inputs, but are classified differently (i.e., misclassified). Both algorithms that create adversarial examples and strategies for defending against adversarial examples typically use L-p-norms to measure the perceptual similarity between an adversarial input and its benign original. Prior work has already shown, however, that two images need not be close to each other as measured by an L-p-norm to be perceptually similar. In this work, we show that nearness according to an L-p-norm is not just unnecessary for perceptual similarity, but is also insufficient. Specifically, focusing on datasets (CIFAR10 and MNIST), L-p-norms, and thresholds used in prior work, we show through online user studies that "adversarial examples" that are closer to their benign counterparts than required by commonly used L-p-norm thresholds can nevertheless be perceptually distinct to humans from the corresponding benign examples. Namely, the perceptual distance between two images that are "near" each other according to an L-p-norm can be high enough that participants frequently classify the two images as representing different objects or digits. Combined with prior work, we thus demonstrate that nearness of inputs as measured by L-p-norms is neither necessary nor sufficient for perceptual similarity, which has implications for both creating and defending against adversarial examples. We propose and discuss alternative similarity metrics to stimulate future research in the area.

关键词： Machine learning Measurement Training computer vision Cancer Object recognition Face recognition

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Aggregating Deep Pyramidal Representations for Person Re-Identification

Aggregating Deep Pyramidal Representations for Person Re-Ide...

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ieee/cvf conference on computer vision and pattern recognition workshops

作者： Niki Martinel Gian Luca Foresti Christian Micheloni Machine Learning and Perception Lab/Artificial Vision and Real-Time Systems Lab University of Udine

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Learning discriminative, view-invariant and multi-scale representations of person appearance with different semantic levels is of paramount importance for person Re-Identification (Re-ID). A surge of effort has been spent by the community to learn deep Re-ID models capturing a holistic single semantic level feature representation. To improve the achieved results, additional visual attributes and body part-driven models have been considered. However, these require extensive human annotation labor or demand additional computational efforts. We argue that a pyramid-inspired method capturing multi-scale information may overcome such requirements. Precisely, multi-scale stripes that represent visual information of a person can be used by a novel architecture factorizing them into latent discriminative factors at multiple semantic levels. A multitask loss is combined with a curriculum learning strategy to learn a discriminative and invariant person representation which is exploited for triplet-similarity learning. Results on three benchmark Re-ID datasets demonstrate that better-performance than existing methods are achieved (e.g., more than 90% accuracy on the Duke-MTMC dataset).

关键词： Persons multi-scale visual information Semantics Body Parts representations Communities

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Dual-Mode Vehicle Motion pattern Learning for High Performance Road Traffic Anomaly Detection 31

Dual-Mode Vehicle Motion Pattern Learning for High Performan...

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

作者： Xu, Yan Ouyang, Xi Cheng, Yu Yu, Shining Xiong, Lin Ng, Choon-Ching Pranata, Sugiri Shen, Shengmei Xing, Junliang Panasonic R&D Ctr Singapore Singapore Singapore Huazhong Univ Sci & Technol Wuhan Hubei Peoples R China Nanyang Technol Univ Singapore Singapore Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Anomaly detection on road traffic is an important task due to its great potential in urban traffic management and road safety. It is also a very challenging task since the abnormal event happens very rarely and exhibits different behaviors. In this work, we present a model to detect anomaly in road traffic by learning from the vehicle motion patterns in two distinctive yet correlated modes, i.e., the static mode and the dynamic mode, of the vehicles. The static mode analysis of the vehicles is learned from the background modeling followed by vehicle detection procedure to find the abnormal vehicles that keep still on the road. The dynamic mode analysis of the vehicles is learned from detected and tracked vehicle trajectories to find the abnormal trajectory which is aberrant from the dominant motion patterns. The results from the dual-mode analyses are finally fused together by driven a re-identification model to obtain the final anomaly. Experimental results on the Track 2 testing set of NVIDIA AI CITY CHALLENGE show the effectiveness of the proposed dual-mode learning model and its robustness in different real scenes. Our result ranks the first place on the final Leaderboard of the Track 2.

关键词： Roads Automobiles Anomaly detection Vehicle dynamics Analytical models Tracking Surveillance

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