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VSB2-Net: Visual-Semantic Bi-Branch Network for Zero-Shot Hashing

VSB2-Net: Visual-Semantic Bi-Branch Network for Zero-Shot Ha...

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international conference on pattern recognition

作者： Xin Li Xiangfeng Wang Bo Jin Wenjie Zhang Jun Wang Hongyuan Zha School of Computer Science and Technology East China Normal University Shanghai China Tencent Shenzhen China College of Computing Georgia Institute of Technology Atlanta GA USA

Zero-shot hashing aims at learning hashing model from seen classes and the obtained model is capable of generalizing to unseen classes for image retrieval. Inspired by zero-shot learning, existing zero-shot hashing methods usually transfer the supervised knowledge from seen to unseen classes, by embedding the hamming space to a shared semantic space. However, this makes instances difficult to distinguish due to limited hashing bit numbers, especially for semantically similar unseen classes. We propose a novel inductive zero-shot hashing framework, i.e., VSB²-Net, where both semantic space and visual feature space are embedded to the same hamming space instead. The reconstructive semantic relationships are established in the hamming space, preserving local similarity relationships and explicitly enlarging the discrepancy between semantic hamming vectors. A two-task architecture, comprising of classification module and visual feature reconstruction module, is employed to enhance the generalization and transfer abilities. Extensive evaluation results on several benchmark datasets demonstrate the superiority of our proposed method compared to several state-of-the-art baselines.

关键词： Visualization Semantics Image retrieval Benchmark testing pattern recognition Acceleration Image reconstruction

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Learning Defects in Old Movies from Manually Assisted Restoration

Learning Defects in Old Movies from Manually Assisted Restor...

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international conference on pattern recognition

作者： Arthur Renaudeau Travis Seng Axel Carlier Fabien Pierre François Lauze Jean-François Aujol Jean-Denis Durou IRIT UMR CNRS 5505 Université de Toulouse France Université de Lorraine CNRS Inria LORIA Nancy France DIKU University of Copenhagen Denmark Université de Bordeaux Bordeaux INP CNRS IMB UMR 5251 Talence France

ISBN: (纸本)9781728188089;9781728188096

We propose to detect defects in old movies, as the first step of a larger framework of old movies restoration by inpainting techniques. The specificity of our work is to learn a film restorer's expertise from a pair of sequences, composed of a movie with defects, and the same movie which was semiautomatically restored with the help of a specialized software. In order to detect those defects with minimal human interaction and further reduce the time spent for a restoration, we feed a U-Net with consecutive defective frames as input to detect the unexpected variations of pixel intensity over space and time. Since the output of the network is a mask of defect location, we first have to create the dataset of mask frames on the basis of restored frames from the software used by the film restorer, instead of classical synthetic ground truth, which is not available. These masks are estimated by computing the absolute difference between restored frames and defectuous frames, combined with thresholding and morphological closing. Our network succeeds in automatically detecting real defects with more precision than the manual selection with an all-encompassing shape, including some the expert restorer could have missed for lack of time.

关键词： Shape Manuals Motion pictures software pattern recognition Feeds

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Characterizing the Role of a Single Coupling Layer in Affine Normalizing Flows 1

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42nd German conference on pattern recognition, DAGM GCPR 2020 held in parallel with 25th international Symposium on Vision, Modeling, and Visualization, VMV 2020 and 10th Eurographics Workshop on Visual computing for Biology and Medicine, VCBM 2020

作者： Draxler, Felix Schwarz, Jonathan Schnörr, Christoph Köthe, Ullrich Heidelberg Collaboratory for Image Processing Heidelberg University Heidelberg Germany Visual Learning Lab Heidelberg University Heidelberg Germany Image and Pattern Analysis Group Heidelberg University Heidelberg Germany

ISBN: (数字)9783030712785

ISBN: (纸本)9783030712778

Deep Affine Normalizing Flows are efficient and powerful models for high-dimensional density estimation and sample generation. Yet little is known about how they succeed in approximating complex distributions, given the seemingly limited expressiveness of individual affine layers. In this work, we take a first step towards theoretical understanding by analyzing the behaviour of a single affine coupling layer under maximum likelihood loss. We show that such a layer estimates and normalizes conditional moments of the data distribution, and derive a tight lower bound on the loss depending on the orthogonal transformation of the data before the affine coupling. This bound can be used to identify the optimal orthogonal transform, yielding a layer-wise training algorithm for deep affine flows. Toy examples confirm our findings and stimulate further research by highlighting the remaining gap between layer-wise and end-to-end training of deep affine flows. © 2021, Springer Nature Switzerland AG.

关键词： Maximum likelihood estimation

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Study on the partial discharge recognition for special-shaped voltage method

Study on the partial discharge recognition for special-shape...

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22nd international Symposium on High Voltage Engineering (ISH 2021)

作者： Y. Wu W. Cao W. Shen Q. Song C. Chen Y. Du Y. Wang School of Electronic Information Xi'an Polytechnic University Xi'an China Electric Power Research Institute State Grid No. 669 Hangtian Middle Road Chang'an District Xi'an China

There is less research on partial discharge (PD) detection under special-shaped wave voltage and corresponding pattern recognition of insulation defects in the current literature. A special-shaped wave voltage method is proposed that is non-destructive testing characteristics, which can be applied to the PD detection of insulation defects. This paper investigates pattern recognition of insulation defects for the special-shaped wave voltage method. First, we analyzed the principle of PD pattern recognition, the PD pattern structure, feature extraction and classifier selection. Subsequently, by using the inherent pattern classification and noise suppression capabilities of the BP neural network, the extracted feature vectors are studied. Finally, the accuracy rate of pattern recognition of PD types of each defect is emphatically compared and analyzed. This study has shown that the pattern recognition algorithm of the BP neural network can be applied to the PD pattern recognition under special-shaped wave voltage of insulation defects, and the accuracy rate of pattern recognition under optimal special-shaped wave voltage is higher than under power frequency AC voltage. Therefore, the PD pattern recognition under special-shaped wave voltage has practical application value.

关键词： backpropagation feature extraction insulation neural nets nondestructive testing partial discharges pattern classification power engineering computing

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Learning from Web Data: Improving Crowd Counting via Semi-Supervised Learning

Learning from Web Data: Improving Crowd Counting via Semi-Su...

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international conference on pattern recognition

作者： Tao Peng Rong Li Shang Li Pengfei Zhu College of Intelligence and Computing Tianjin University Tianjin China 63961 Troops of P.L.A China Innovation & Research Institute of HIWING Technology Academy China

Deep neural networks have been widely used in crowd counting that aims to give the number of objects in images and videos. The performance of crowd counting models is greatly affected by the size of datasets with high quality annotations. However, collecting and annotating large-scale crowd counting dataset is labor-intensive and time-consuming. In this work, we exploit unlabeled web images to boost the performance of crowd counting models in a semi-supervised manner. Based on the observation that the rotation and splitting operations will not change the number of object in image, we design three auxiliary tasks to improve the feature representation ability of deep models. A semi-supervised multi-task learning framework is proposed by introducing three auxiliary tasks with respect to unlabeled data and our framework can be easily extended to other crowd counting models. An unlabeled dataset (Web-Crowd) with 8679 web images are collected for semi-supervised crowd counting. Experiments shows that our semi-supervised multi-task learning framework can effectively boost the performance of crowd counting models on UCF-QNRF dataset and ShanghaiTech dataset.

关键词： Visualization Annotations Neural networks Semisupervised learning Data models pattern recognition Task analysis

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A Novel Method for Extracting Features of Partial Discharge Pulse Current Signal

A Novel Method for Extracting Features of Partial Discharge ...

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Advanced Electrical Equipment and Reliable Operation (AEERO), international conference on

作者： Yinan Wu Haibao Mu Yiming Zheng Jiangyang Zhan Xianjun Shao Chen Li Chi Zhang Guanjun Zhang State key Laboratory of Electrical Insulation and Power Equipment Xi’an Jiaotong University Xi’an China State Grid Zhejiang Electric Power Research Institute Zhejiang China

ISBN: (纸本)9781665402651

Partial discharge (PD) not only causes the aging of the internal insulation of the power transformer but also characterizes the deterioration of the insulation system. During the operation of the power transformer, different types of partial discharges (PDs) may occur inside. The opportune PD pattern recognition is of great significance to the maintenance and operation stability of the transformer. The pulse current method is a widely used PD detection method, and the feature extraction of pulse current signal is a key step in PD pattern recognition. The present feature extraction method of pulse current signal usually uses a complex algorithm and takes a long computing time, which is not suitable for field application. This paper proposed a fast and effective method for extracting features of PD pulse current signals. The features extracted by this method can correctly classify the three typical insulation defects in transformers.

关键词： Partial discharges Insulation Maintenance engineering Aging Feature extraction pattern recognition Classification algorithms

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DFGC 2022: The Second DeepFake Game Competition

DFGC 2022: The Second DeepFake Game Competition

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IEEE international Joint conference on Biometrics (IJCB)

作者： Bo Peng Wei Xiang Yue Jiang Wei Wang Jing Dong Zhenan Sun Zhen Lei Siwei Lyu Center for Research on Intelligent Perception and Computing (CRIPAC) Institute of Automation Chinese Academy of Sciences Beijing China National Laboratory of Pattern Recognition (NLPR) Institute of Automation Chinese Academy of Sciences Beijing China University at Buffalo State University of New York USA

ISBN: (纸本)9781665463959

This paper presents the summary report on our DFGC 2022 competition. The DeepFake is rapidly evolving, and realistic face-swaps are becoming more deceptive and difficult to detect. On the other hand, methods for detecting DeepFakes are also improving. There is a two-party game between DeepFake creators and defenders. This competition provides a common platform for benchmarking the game between the current state-of-the-arts in Deep-Fake creation and detection methods. The main research question to be answered by this competition is the current state of the two adversaries when competed with each other. This is the second edition after the last year's DFGC 2021, with a new, more diverse video dataset, a more realistic game setting, and more reasonable evaluation metrics. With this competition, we aim to stimulate research ideas for building better defenses against the DeepFake threats. We also release our DFGC 2022 dataset contributed by both our participants and ourselves to enrich the DeepFake data resources for the research community (https://***/NiCE-X/DFGC-2022).

关键词： Measurement Video coding Deepfakes Correlation Superresolution Mouth Games

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VOWEL: A Local Online Learning Rule for Recurrent Networks of Probabilistic Spiking Winner- Take-All Circuits

VOWEL: A Local Online Learning Rule for Recurrent Networks o...

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international conference on pattern recognition

作者： Hyeryung Jang Nicolas Skatchkovsky Osvaldo Simeone KCLIP lab King's College London London United Kingdom

Networks of spiking neurons and Winner- Take-All spiking circuits (WTA -SNNs) can detect information encoded in spatio-temporal multi-valued events. These are described by the timing of events of interest, e.g., clicks, as well as by categorical numerical values assigned to each event, e.g., like or dislike. Other use cases include object recognition from data collected by neuromorphic cameras, which produce, for each pixel, signed bits at the times of sufficiently large brightness variations. Existing schemes for training WTA -SNNs are limited to rate-encoding solutions, and are hence able to detect only spatial patterns. Developing more general training algorithms for arbitrary WTA -SNNs inherits the challenges of training (binary) Spiking Neural Networks (SNNs). These amount, most notably, to the non-differentiability of threshold functions, to the recurrent behavior of spiking neural models, and to the difficulty of implementing backpropagation in neuromorphic hardware. In this paper, we develop a variational online local training rule for WTA-SNNs, referred to as VOWEL, that leverages only local pre- and post-synaptic information for visible circuits, and an additional common reward signal for hidden circuits. The method is based on probabilistic generalized linear neural models, control variates, and variational regularization. Experimental results on real-world neuromorphic datasets with multi-valued events demonstrate the advantages of WTA-SNNs over conventional binary SNNs trained with state-of-the-art methods, especially in the presence of limited computing resources.

关键词： Training Neuromorphics Neurons Probabilistic logic Hardware Timing pattern recognition

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Second-order Attention Guided Convolutional Activations for Visual recognition

Second-order Attention Guided Convolutional Activations for ...

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international conference on pattern recognition

作者： Shannan Chen Qian Wang Qiule Sun Bin Liu Jianxin Zhang Qiang Zhang Key Lab of Advanced Design and Intelligent Computing (Ministry of Education) Dalian University Dalian China School of Information and Communication Engineering Dalian University of Technology Dalian China International School of Information Science and Engineering (DUT-RUISE) Dalian University of Technology Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

Recently, modeling deep convolutional activations by the global second-order pooling has shown great advance on visual recognition tasks. However, most of the existing deep second-order statistical models mainly compute second-order statistics of activations of the last convolutional layer as image representations, and they seldom introduce second-order statistics into earlier layers to better fit network topology, thus limiting the representational ability to a certain extent. Motivated by the flexibility of attention blocks that are commonly plugged into intermediate layers of deep convolutional networks (ConvNets), this work makes an attempt to combine deep second-order statistics with attention mechanisms in ConvNets, and further proposes a novel Second-order Attention Guided Network (SoAG-Net) for visual recognition. More specifically, SoAG-Net involves several SoAG modules seemingly inserted into intermediate layers of the network, in which SoAG collects second-order statistics of convolutional activations by polynomial kernel approximation to predict channel-wise attention maps utilized for guiding the learning of convolutional activations through tensor scaling along channel dimension. SoAG improves the nonlinearity of ConvNets and enables ConvNets to fit more complicated distribution of convolutional activations. Experiment results on three commonly used datasets illuminate that SoAG-Net outperforms its counterparts and achieves competitive performance with state-of-the-art models under the same backbone.

关键词： Visualization Tensors Limiting Network topology Computational modeling Image representation pattern recognition

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Jointly Learning Multiple Curvature Descriptor for 3D Palmprint recognition

Jointly Learning Multiple Curvature Descriptor for 3D Palmpr...

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international conference on pattern recognition

作者： Lunke Fei Jianyang Qin Peng Liu Jie Wen Chunwei Tian Bob Zhang Shuping Zhao School of Computer Science and Technology Guangdong University of Technology Guangzhou China The Bio-Computing Research Center Harbin Institute of Technology(Shenzhen) Shenzhen China University of Macau Taipa Macau China

3D palmprint-based biometric recognition has drawn growing research attention due to its several merits over 2D counterpart such as robust structural measurement of a palm surface and high anti-counterfeiting capability. However, most existing 3D palmprint descriptors are hand-crafted that usually extract stationary features from 3D palmprint images. In this paper, we propose a feature learning method to jointly learn compact curvature feature descriptor for 3D palmprint recognition. We first form multiple curvature data vectors to completely sample the intrinsic curvature information of 3D palmprint images. Then, we jointly learn a feature projection function that project curvature data vectors into binary feature codes, which have the maximum inter-class variances and minimum intra-class distance so that they are discriminative. Moreover, we learn the collaborative binary representation of the multiple curvature feature codes by minimizing the information loss between the final representation and the multiple curvature features, so that the proposed method is more compact in feature representation and efficient in matching. Experimental results on the baseline 3D palmprint database demonstrate the superiority of the proposed method in terms of recognition performance in comparison with state-of-the-art 3D palmprint descriptors.

关键词： Three-dimensional displays Palmprint recognition Databases Collaboration Binary codes Feature extraction pattern recognition

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