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检索条件"任意字段=7th Chinese Conference on Pattern Recognition and Computer Vision"

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Multi-strategy Knowledge Distillation Based Teacher-Student Framework for Machine Reading Comprehension 20

Multi-strategy Knowledge Distillation Based Teacher-Student ...

引用

20th China National conference on Computational Linguistics, CCL 2021

作者： Yu, Xiaoyan Liu, Qingbin He, Shizhu Liu, Kang Liu, Shengping Zhao, Jun Zhou, Yongbin School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security Nanjing University of Science and Technology Nanjing China Beijing Unisound Information Technology Co. Ltd. Beijing China

ISBN: (纸本)9783030841850

the irrelevant information in documents poses a great challenge for machine reading comprehension (MRC). To deal with such a challenge, current MRC models generally fall into two separate parts: evidence extraction and answer prediction, where the former extracts the key evidence corresponding to the question, and the latter predicts the answer based on those sentences. However, such pipeline paradigms tend to accumulate errors, i.e. extracting the incorrect evidence results in predicting the wrong answer. In order to address this problem, we propose a Multi-Strategy Knowledge Distillation based Teacher-Student framework (MSKDTS) for machine reading comprehension. In our approach, we first take evidence and document respectively as the input reference information to build a teacher model and a student model. then the multi-strategy knowledge distillation method transfers the knowledge from the teacher model to the student model at both feature and prediction level through knowledge distillation approach. therefore, in the testing phase, the enhanced student model can predict answer similar to the teacher model without being aware of which sentence is the corresponding evidence in the document. Experimental results on the ReCO dataset demonstrate the effectiveness of our approach, and further ablation studies prove the effectiveness of both knowledge distillation strategies. © 2021, Springer Nature Switzerland AG.

关键词： Distillation

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Facial Acupoints Location using Transfer Learning on Deep Residual Network

Facial Acupoints Location using Transfer Learning on Deep Re...

引用

International conference on computer and Communications (ICCC)

作者： Yanglu Chen Hongyu Yang Dongnan Chen Xiyu Chen National Key Laboratory of Fundamental Science on Synthetic Vision Sichuan University Chengdu China College of Acupuncture and Massage Chengdu University of TCM Chengdu China Computer Science College Sichuan University Chengdu China

ISBN: (纸本)9781665409513

In the field of traditional chinese medicine, whether it’s acupuncture, moxibustion or massage health care, are inseparable from acupoints location. In this paper, we combine traditional chinese medicine with artificial intelligence, proposing a facial acupoints location method on deep residual network. We also create a facial acupoints dataset containing 1040 face images and 9 acupoints. Based on transfer learning, we first train a facial landmarks location network using a facial landmarks dataset. then, we transfer the learned low-level features to facial acupoints location network, and use an adaptive segmented Wingloss function to reducing the location error. Our experiments indicate that transfer learning and Wingloss function are effective in improving the accuracy of facial acupoints location. the methods used by other researchers often have errors caused by intermediate variables, and most of their studies have no scientific location accuracy index. We have improved these problems, and the normalized average error of the facial acupoints location is 2.5% in our research.

关键词： Image segmentation Adaptive systems Face recognition Transfer learning Medical services Indexes Artificial intelligence

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Gaussian Constrained Attention Network for Scene Text recognition

Gaussian Constrained Attention Network for Scene Text Recogn...

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International conference on pattern recognition

作者： Zhi Qiao Xugong Qin Yu Zhou Fei Yang Weiping Wang School of Cyber Security University of Chinese Academy of Sciences Institute of Information Engineering Chinese Academy of Sciences TAL Education Group

ISBN: (纸本)9781728188089;9781728188096

Scene text recognition has been a hot topic in computer vision. Recent methods adopt the attention mechanism for sequence prediction which achieve convincing results. However, we argue that the existing attention mechanism faces the problem of attention diffusion, in which the model may not focus on a certain character area. In this paper, we propose Gaussian Constrained Attention Network to deal with this problem. It is a 2D attention-based method integrated with a novel Gaussian Constrained Refinement Module, which predicts an additional Gaussian mask to refine the attention weights. Different from adopting an additional supervision on the attention weights simply, our proposed method introduces an explicit refinement. In this way, the attention weights will be more concentrated and the attention-based recognition network achieves better performance. the proposed Gaussian Constrained Refinement Module is flexible and can be applied to existing attention-based methods directly. the experiments on several benchmark datasets demonstrate the effectiveness of our proposed method. Our code has been available at https://***/Pay20Y/GCAN.

关键词： computer vision Text recognition Computational modeling Benchmark testing Performance gain Decoding Faces

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Domain Decomposition Based Preconditioned Solver for Bundle Adjustment 7th

Domain Decomposition Based Preconditioned Solver for Bundle ...

引用

7th National conference on computer vision, pattern recognition, Image Processing, and Graphics, NCVPRIPG 2019

作者： Das, Shrutimoy Katyan, Siddhant Kumar, Pawan International Institute of Information Technology Hyderabad500032 India

ISBN: (纸本)9789811586965

We propose Domain Decomposed Bundle Adjustment (DDBA), a robust and efficient solver for the bundle adjustment problem. Bundle adjustment (BA) is generally formulated as a non-linear least squares problem and is solved by some variant of the Levenberg-Marquardt (LM) algorithm. Each iteration of the LM algorithm requires solving a system of normal equations, which becomes computationally expensive with the increase in problem size. the coefficient matrix of this system has a sparse structure which can be exploited for simplifying the computations in this step. We propose a technique for approximating the Schur complement of the matrix, and use this approximation to construct a preconditioner, that can be used with the Generalized Minimal Residual (GMRES) algorithm for solving the system of equations. Our experiments on the BAL dataset show that the proposed method for solving the system is faster than GMRES solve preconditioned with block Jacobi and more memory efficient than direct solve. © 2020, Springer Nature Singapore Pte Ltd.

关键词： computer vision

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UHRSNet: A Semantic Segmentation Network Specifically for Ultra-High-Resolution Images

UHRSNet: A Semantic Segmentation Network Specifically for Ul...

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International conference on pattern recognition

作者： Lianlei Shan Minglong Li Xiaobin Li Yang Bai Ke Lv Bin Luo Si-Bao Chen Weiqiang Wang University of Chinese Academy of Sciences Beijing China MOE Key Lab of Signal Processing and Intelligent Computing School of Computer Science and Technology Anhui University Hefei China

Semantic segmentation is a basic task in computer vision, but only limited attention has been devoted to the ultra-high-resolution (UHR) image segmentation. Since UHR images occupy too much memory, they cannot be directly put into GPU for training. Previous methods are cropping images to small patches or downsampling the whole images. Cropping and downsampling cause the loss of contexts and details, which is essential for segmentation accuracy. To solve this problem, we improve and simplify the local and global feature fusion method in previous works. Local features are extracted from patches and global features are from downsampled images. Meanwhile, we propose one new fusion called local feature fusion for the first time, which can make patches get information from surrounding patches. We call the network with these two fusions ultra-high-resolution segmentation network (UHRSNet). these two fusions can effectively and efficiently solve the problem caused by cropping and downsampling. Experiments show a remarkable improvement on Deepglobe dataset [1].

关键词： Training Image segmentation computer vision Semantics Graphics processing units Feature extraction pattern recognition

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Semantic Inpainting with Multi-dimensional Adversarial Network and Wasserstein Distance 3rd

Semantic Inpainting with Multi-dimensional Adversarial Netwo...

引用

3rd chinese conference on pattern recognition and computer vision (PRCV)

作者： Wang, Haodi Jiao, Libin Bie, Rongfang Wu, Hao Beijing Normal Univ Coll Artificial Intelligence Beijing Peoples R China Chinese Acad Sci Inst Remote Sensing & Digital Earth RADI Beijing Peoples R China

ISBN: (纸本)9783030606350;9783030606367

Inpainting represents a procedure which can restore the lost parts of an image based upon the residual information. We present an inpainting network that consists of an Encoder-Decoder pipeline and a multi-dimensional adversarial network. the Encoder-Decoder pipeline extracts features from the input image with missing area and learns these features. through unsupervised learning, the pipeline can predict and fill the missing region with the most reasonable content. Meanwhile the multi-dimensional adversarial network identifies the difference between the ground truth and the generated images both in detail and in general. Compared with the traditional training procedure, our model combines with Wasserstein Distance that enhances the stability of network training. the network is training specifically on street view images and not only performs a satisfying outcome, but also shows competitiveness when comparing with existing methods.

关键词： Inpainting Multi-dimensional discriminator Wasserstein distance

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A Novel Set of Moment Invariants for pattern recognition Applications Based on Jacobi Polynomials 12th

A Novel Set of Moment Invariants for Pattern Recognition App...

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12th Mexican conference on pattern recognition (MCPR)

作者： Rocha Angulo, Rafael Augusto Martin Carpio, Juan Rojas-Dominguez, Alfonso Ornelas-Rodriguez, Manuel Puga, Hector Tecnol Nacl Mexico Inst Tecnol Leon Leon 37290 Gto Mexico

ISBN: (纸本)9783030490768;9783030490751

A novel set of moment invariants for pattern recognition applications, which are based on Jacobi polynomials, are presented. these moment invariants are constructed for digital images by means of a combination with geometric moments, and are invariant in the face of affine geometric transformations such as rotation, translation and scaling, on the image plane. this invariance is tested on a sample of the MPEG-7 CE-Shape-1 dataset. the results presented show that the low-order moment invariants indeed possess low variance between images that are affected by the mentioned geometric transformations.

关键词： Jacobi polynomials Orthogonal polynomials Geometric moment invariants Jacobi moment invariants Moments

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the 2nd 106-Point Lightweight Facial Landmark Localization Grand Challenge 25th

The 2nd 106-Point Lightweight Facial Landmark Localization G...

引用

25th International conference on pattern recognition Workshops, ICPR 2020

作者： Liu, Yinglu Li, Peipei Tong, Xin Shi, Hailin Zhu, Xiangyu Sun, Zhenan Xu, Zhen Liu, Huaibo Su, Xuefeng Chen, Wei Huang, Han Wang, Duomin Tao, Xunqiang Guo, Yandong Tong, Ziye Lai, Shenqi Chai, Zhenhua JD AI Research Beijing China Institute of Automation Chinese Academy of Sciences Beijing China SogouAI Beijing China OPPO Research Institute Beijing China Vision Intelligence Center Meituan Beijing China

ISBN: (纸本)9783030687922

Facial landmark localization has been applied to numerous face related applications, such as face recognition and face image synthesis. It is a very crucial step for achieving high performance in these applications. We host the 2nd 106-point lightweight facial landmark localization grand challenge in conjunction with ICPR 2020. the purpose is to make effort towards benchmarking lightweight facial landmark localization, which enables efficient system deployment. Compared with the 1st grand challenge (https://***/ ), the JD-landmark-v2 dataset contains more than 24,000 images with larger variations in identity, pose, expression and occlusion. Besides, strict limits of model size (≤ 20M) and computational complexity (≤ 1G Flops) are employed for computational efficiency. the challenge has attracted attention from academia and industrial practitioners. More than 70 teams participate in the competition, and nine of them involve in the final evaluation. We give a detailed introduction of the competition and the solution by the winners in this paper. © 2021, Springer Nature Switzerland AG.

关键词： Face recognition

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A review of human face detection in complex environment 6th

A review of human face detection in complex environment

引用

6th International conference on Artificial Intelligence and Security,ICAIS 2020

作者： Chen, Long Liu, Yuling Xin, Guojiang College of Computer Science and Electronic Engineering Hunan University Changsha410082 China School of Informatics Hunan University of Chinese Medicine Changsha410208 China

ISBN: (纸本)9789811581007

Facial recognition technology has always been a challenging field in computer vision and pattern recognition. At present, facial recognition technology has been widely used in daily life. Much research has been done on facial recognition systems. However, we still need to continuously enhance and improve the facial recognition system in practical applications such as insufficient lighting and incomplete facial images. the main purpose of this article is to introduce the latest advances in face detection methods based on complex backgrounds, including feature-based, knowledge-based and appearance-based method. © Springer Nature Singapore Pte Ltd 2020.

关键词： Face recognition

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Building Computationally Efficient and Well-Generalizing Person Re-Identification Models with Metric Learning

Building Computationally Efficient and Well-Generalizing Per...

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International conference on pattern recognition

作者： Vladislav Sovrasov Dmitry Sidnev IOTG Computer Vision (ICV) Intel

this work considers the problem of domain shift in person re-identification. Being trained on one dataset, a reidentification model usually performs much worse on unseen data. Partially this gap is caused by the relatively small scale of person re-identification datasets (compared to face recognition ones, for instance), but it is also related to training objectives. We propose to use the metric learning objective, namely AMSoftmax loss, and some additional training practices to build wellgeneralizing, yet, computationally efficient models. We use recently proposed Omni-Scale Network (OSNet) architecture combined with several training tricks and architecture adjustments to obtain state-of-the art results in cross-domain generalization problem on a large-scale MSMTI7 dataset in three setups: MSMTI7-all→DukeMTMC, MSMTI7-train→MarketI5OI and MSMTI7-all→MarketI5OI. Training code and the models are available online in the Github repository¹.

关键词： Training Measurement Computational modeling Architecture Face recognition computer architecture Feature extraction

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