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检索条件"任意字段=2021 10th International Conference on Computing and Pattern Recognition"

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JUMPS: Joints Upsampling Method for Pose Sequences

JUMPS: Joints Upsampling Method for Pose Sequences

引用

international conference on pattern recognition

作者： Lucas Mourot François Le Clerc Cédric thébault Pierre Hellier InterDigital Rennes France

ISBN: (纸本)9781728188089;9781728188096

Human Pose Estimation is a low-level task useful for surveillance, human action recognition, and scene understanding at large. It also offers promising perspectives for the animation of synthetic characters. For all these applications, and especially the latter, estimating the positions of many joints is desirable for improved performance and realism. To this purpose, we propose a novel method called JUMPS for increasing the number of joints in 2D pose estimates and recovering occluded or missing joints. We believe this is the first attempt to address the issue. We build on a deep generative model that combines a Generative Adversarial Network (GAN) and an encoder. the GAN learns the distribution of high-resolution human pose sequences, the encoder maps the input low-resolution sequences to its latent space. Inpainting is obtained by computing the latent representation whose decoding by the GAN generator optimally matches the joints locations at the input. Post-processing a 2D pose sequence using our method provides a richer representation of the character motion. We show experimentally that the localization accuracy of the additional joints is on average on par with the original pose estimates.

关键词： three-dimensional displays Recurrent neural networks Surveillance Pose estimation Machine learning Network architecture Generative adversarial networks

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Exploration on Cloud Resource Platform-Based Cultivation pattern for the Lifelong Learning of Vocational Teaching Staff

Exploration on Cloud Resource Platform-Based Cultivation Pat...

引用

international conference on Educational and Information Technology (ICEIT)

作者： Qiu Li Liyuan Shang School of Mechanical Engineering Tianjin University of Technology and Education Tianjin China

ISBN: (数字)9781665422956

ISBN: (纸本)9781665446471

the cultivation of the lifelong learning literacy is an irresistible trend and requirement of promoting the individual competitiveness of teachers in vocational institutions, as well the overall development level of the vocational education. By taking the program of numerical control programming (NC programming) as an example, this paper studied the lifelong learning pattern based on the cloud platform of Internet resources. Cloud resource platform ran through full cultivation process of the teaching staff in their development stage from students to teachers. It is of great significance to achieve the integrated studies and cultivation for individual vocational education in terms of knowledge, skills and qualities, and promote the integration between production and education. Its application effectively mobilized the initiatives of students, teachers of vocational schools and enterprises, and developed a new channel for the lifelong learning of normal university students oriented for vocational schools and vocational school teachers.

关键词： Cloud computing Education Production Market research Information technology Computer numerical control Programming profession

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IBD: A Feedback Framework with Deep-learning for IoT-generated Big Data Processing

IBD: A Feedback Framework with Deep-learning for IoT-generat...

引用

international conference on System Modeling & Advancement in Research Trends (SMART)

作者： Vinay Kumar Mishra Vivek Kumar Neeraj Kumar Pandey Computer Science and IT Teerthanker Mahaveer University Moradabad India Computer Science and Engineering Teerthanker Mahaveer University Moradabad India School of Computing DIT University Dehradun India

ISBN: (纸本)9781665439718

Convolutional Neural Network (CNN) and Recurrent Neural Networks (RNNs)have the ability to find the accurate result in images and text respectively. the best classification results are still awaited due to the high cost of computation and high memory requirements of CNN and RNN. Our work suggests a framework that improves the quality of data at various layers by providing feedback to suggested system. the proposed framework leads to an error free processing system.

关键词： Deep learning Actuators Recurrent neural networks Costs Computational modeling Memory management Big Data

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Global-Local Attention Network for Semantic Segmentation in Aerial Images

Global-Local Attention Network for Semantic Segmentation in ...

引用

international conference on pattern recognition

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

Errors in semantic segmentation could be classified into two types: the large area misclassification and inaccurate local boundaries. Previously attention-based methods typically capture rich global contextual information, which benefits the large area classification but cannot address the local errors of boundaries. In this paper, we propose a Global-Local Attention Network (GLANet) which can simultaneously consider the global context and local details. Specifically, our GLANet consists of two branches: (1) the global attention branch and (2) local attention branch. Furthermore, three different modules are embedded in GLANet for respectively modelling the semantic interdependencies in spatial, channel and boundary dimension. Lastly, we merge the outputs of different branches to enhance the feature representation further, resulting in more precise segmentation. Overall, the proposed method achieves the competitive segmentation accuracy on two public aerial image datasets, bringing significant improvements over the existing baselines.

关键词： Image segmentation Image color analysis Image edge detection Semantics Vegetation mapping Vegetation pattern recognition

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Semi-Supervised Class Incremental Learning

Semi-Supervised Class Incremental Learning

引用

international conference on pattern recognition

作者： Alexis Lechat Stéphane Herbin Frédéric Jurie Normandie Univ UNICAEN ENSICAEN CNRS GREYC France DTIS ONERA Université Paris-Saclay France

this paper makes a contribution to the problem of incremental class learning, the principle of which is to sequentially introduce batches of samples annotated with new classes during the learning phase. the main objective is to reduce the drop in classification performance on old classes, a phenomenon commonly called catastrophic forgetting. We propose in this paper a new method which exploits the availability of a large quantity of non-annotated images in addition to the annotated batches. these images are used to regularize the classifier and give the feature space a more stable structure. We demonstrate on two image data sets, MNIST and STL-10, that our approach is able to improve the global performance of classifiers learned using an incremental learning protocol, even with annotated batches of small size.

关键词： Training Protocols pattern recognition Image reconstruction

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SCA Net: Sparse Channel Attention Module for Action recognition

SCA Net: Sparse Channel Attention Module for Action Recognit...

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international conference on pattern recognition

作者： Hang Song YongHong Song YuanLin Zhang College of Artificial Intelligence Xi'an Jiaotong University Xi'an China

Channel attention has shown its great performance recently when it was incorporated into deep convolutional neural networks. However, existing methods usually require extensive computing resources due to their involuted structure, which further increase the computational burden of 3D CNNs. In this paper, a lightweight sparse channel attention (SCA) module implemented by efficient group convolution is proposed, which adopts the idea of sparse channel connection and involves much fewer parameters but brings clear performance gain. Meanwhile, to solve the lack of local channel interaction brought by group convolution, a dominant function called Aggregate-Shuffle-Diverge (ASD) is leveraged to enhance information flow over each group with no additional parameters. We also adjust the existing mainstream 3D CNNs by employing 3D convolution factorization, so as to further reduce the parameters. Our SCA module can be flexibly incorporated into most existing 3D CNNs, all of which can achieve a perfect trade-off between performance and complexity on action recognition task with factorized I3D or 3D ResNext backbone networks. the experimental results also indicate that the resulting network, namely, SCA Net can achieve outstanding performance on UCF-101 and HMDB-51 datasets.

关键词： three-dimensional displays Correlation Convolution Performance gain Complexity theory pattern recognition Convolutional neural networks

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Boundary bagging to address training data issues in ensemble classification

Boundary bagging to address training data issues in ensemble...

引用

international conference on pattern recognition

作者： Samia Boukir Wei Feng Bordeaux INP G&E Lab EA 4592 Pessac France School of Electronic Engineering Xidian University Xian China

the characteristics of training data is a fundamental consideration when constructing any supervised classifier. Class mislabelling and imbalance are major training data issues that often adversely affect machine learning algorithms, including ensembles. this work proposes extended bagging algorithms to better handle noisy and multi-class imbalanced classification tasks. these algorithms upgrade the sampling procedure by taking benefit of the confidence in ensemble classification outcome. the underlying idea is that a bagging ensemble learning algorithm can achieve greater performance if it is allowed to choose the data from which it learns. the effectiveness of the proposed methods is demonstrated in performing classification on 10 various data sets.

关键词： Machine learning algorithms Training data Classification algorithms pattern recognition Noise measurement Task analysis Bagging

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Top-DB-Net: Top DropBlock for Activation Enhancement in Person Re-Identification

Top-DB-Net: Top DropBlock for Activation Enhancement in Pers...

引用

international conference on pattern recognition

作者： Rodolfo Quispe Helio Pedrini Institute of Computing University of Campinas Campinas SP Brazil

Person Re-Identification is a challenging task that aims to retrieve all instances of a query image across a system of non-overlapping cameras. Due to the various extreme changes of view, it is common that local regions that could be used to match people are suppressed, which leads to a scenario where approaches have to evaluate the similarity of images based on less informative regions. In this work, we introduce the Top-DB-Net, a method based on Top DropBlock that pushes the network to learn to focus on the scene foreground, with special emphasis on the most task-relevant regions and, at the same time, encodes low informative regions to provide high discriminability. the Top-DB-Net is composed of three streams: (i) a global stream encodes rich image information from a backbone, (ii) the Top DropBlock stream encourages the backbone to encode low informative regions with high discriminative features, and (iii) a regularization stream helps to deal with the noise created by the dropping process of the second stream, when testing the first two streams are used. Vast experiments on three challenging datasets show the capabilities of our approach against state-of-the-art methods. Qualitative results demonstrate that our method exhibits better activation maps focusing on reliable parts of the input images. the source code is available at: https://***/RQuispeC/top-dropblock.

关键词： Focusing Streaming media Cameras pattern recognition Reliability Task analysis Testing

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Semi-supervised Person Re-identification by Attribute Similarity Guidance

Semi-supervised Person Re-identification by Attribute Simila...

引用

international conference on pattern recognition

作者： Peixian Hong Ancong Wu Wei-Shi Zheng School of Data and Computer Science Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

Although supervised person re-identification (RE-ID) has achieved great progress with deep learning, it requires time-consuming annotation of a large number of pedestrian identities. To reduce labeling cost, we attempt to reduce cross-camera identity annotations and exploit pedestrian attribute annotations as auxiliary information instead. the pedestrian attributes, such as outfit styles, contain coarse semantic knowledge. Although pedestrian attributes are annotated without exhaustive searching in a camera network, which is much easier than cross-camera identity annotation, ambiguity exists in attributes when different persons have similar outfits. To solve this problem, we propose an Attribute Similarity Guidance loss (ASG) to guide appearance feature learning for RE-ID by selective attribute similarity preservation to avoid the impact of such ambiguity. Finally, we develop an attribute-guided self training framework to jointly utilize attribute annotations, unlabeled data and limited labeled data for semi-supervised learning. Extensive experiments on Market-1501 and DukeMTMC-ReID show the superiority of our method for semi-supervised RE-ID.

关键词： Training Deep learning Annotations Semantics Semisupervised learning Cameras pattern recognition

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Edge-Aware Monocular Dense Depth Estimation with Morphology

Edge-Aware Monocular Dense Depth Estimation with Morphology

引用

international conference on pattern recognition

作者： Zhi Li Xiaoyang Zhu Haitao Yu Qi Zhang Yongshi Jiang Institute of Automation Chinese Academy of Sciences

Dense depth maps play an important role in Computer Vision and AR (Augmented Reality). For CV applications, a dense depth map is the cornerstone of 3D reconstruction allowing real objects to be precisely displayed in the computer. And Dense depth maps can handle correct occlusion relationships between virtual content and real objects for better user experience in AR. However, the complicated computation limits the development of computing dense depth maps. We present a novel algorithm that produces low latency, spatio-temporally smooth dense depth maps using only a CPU. the depth maps exhibit sharp discontinuities at depth edges in low computational complexity ways. Our algorithm obtains the sparse SLAM reconstruction first, then extracts coarse depth edges from a down-sampled RGB image by morphology operations. Next, we thin the depth edges and align them with image edges. Finally, an effective initialization scheme and an improved optimization solver are adopted to accelerate convergence. We evaluate our proposal quantitatively and the result shows improvements on the accuracy of depth map with respect to other state-of-the-art and baseline techniques.

关键词： three-dimensional displays Simultaneous localization and mapping Image edge detection Morphology Estimation User experience pattern recognition

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