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Occlusion-Aware Siamese Network for Human Pose Estimation 16th

Occlusion-Aware Siamese Network for Human Pose Estimation

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16th European conference on computer vision, ECCV 2020

作者： Zhou, Lu Chen, Yingying Gao, Yunze Wang, Jinqiao Lu, Hanqing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China ObjectEye Inc. Beijing China NEXWISE Co. Ltd. Guangzhou China

ISBN: (纸本)9783030585648

Pose estimation usually suffers from varying degrees of performance degeneration owing to occlusion. To conquer this dilemma, we propose an occlusion-aware siamese network to improve the performance. Specifically, we introduce scheme of feature erasing and reconstruction. Firstly, we utilize attention mechanism to predict the occlusion-aware attention map which is explicitly supervised and clean the feature map which is contaminated by different types of occlusions. Nevertheless, the cleaning procedure not only removes the useless information but also erases some valuable details. To overcome the defects caused by the erasing operation, we perform feature reconstruction to recover the information destroyed by occlusion and details lost in cleaning procedure. To make reconstructed features more precise and informative, we adopt siamese network equipped with OT divergence to guide the features of occluded images towards those of the un-occluded images. Algorithm is validated on MPII, LSP and COCO benchmarks and we achieve promising results. © 2020, Springer Nature Switzerland AG.

关键词： computers

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Blended Grammar Network for Human Parsing 1

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16th European conference on computer vision, ECCV 2020

作者： Zhang, Xiaomei Chen, Yingying Zhu, Bingke Wang, Jinqiao Tang, Ming National Laboratory of Pattern Recognition Institute of Automation CAS Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China ObjectEye Inc. Beijing China NEXWISE Co. Ltd. Guangzhou China

ISBN: (数字)9783030585860

ISBN: (纸本)9783030585853

Although human parsing has made great progress, it still faces a challenge, i.e., how to extract the whole foreground from similar or cluttered scenes effectively. In this paper, we propose a Blended Grammar Network (BGNet), to deal with the challenge. BGNet exploits the inherent hierarchical structure of a human body and the relationship of different human parts by means of grammar rules in both cascaded and paralleled manner. In this way, conspicuous parts, which are easily distinguished from the background, can amend the segmentation of inconspicuous ones, improving the foreground extraction. We also design a Part-aware Convolutional Recurrent Neural Network (PCRNN) to pass messages which are generated by grammar rules. To train PCRNNs effectively, we present a blended grammar loss to supervise the training of PCRNNs. We conduct extensive experiments to evaluate BGNet on PASCAL-Person-Part, LIP, and PPSS datasets. BGNet obtains state-of-the-art performance on these human parsing datasets. © 2020, Springer Nature Switzerland AG.

关键词： Recurrent neural networks

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CloudifierNet - Deep vision Models for Artificial Image Processing 7

CloudifierNet - Deep Vision Models for Artificial Image Proc...

引用

7th International conference on Information Technology and Quantitative Management (ITQM) - Information Technology and Quantitative Management Based on Artificial Intelligence

作者： Damian, Andrei Ionut Piciu, Laurentiu Purdila, Alexandru Tapus, Nicolae Lummetry AI Bucharest Romania Univ Politehn Bucuresti Bucharest Romania

Today, more and more, it is necessary that most applications and documents developed in previous or current technologies to be accessible online on cloud-based infrastructures. that's why the migration of legacy systems including their hosts of documents to new technologies and online infrastructures, using modem Artificial Intelligence techniques, is absolutely necessary. With the advancement of Artificial Intelligence and Deep Learning with its multitude of applications, a new area of research is emerging that of automated systems development and maintenance. the underlying work objective that led to this paper aims to research and develop truly intelligent systems able to analyze user interfaces from various sources and generate real and usable inferences ranging from architecture analysis to actual code generation. One key element of such systems is that of artificial scene detection and analysis based on deep learning computer vision systems. computer vision models and particularly deep directed acyclic graphs based on convolutional modules are generally constructed and trained based on natural images datasets. Due to this fact, the models will develop during the training process natural image feature detectors apart from the base graph modules that will learn basic primitive features. In the current paper, we will present the base principles of a deep neural pipeline for computer vision applied to artificial scenes (scenes generated by user interfaces or similar). Finally, we will present the conclusions based on experimental development and benchmarking against state-of-the-art transfer-learning implemented deep vision models. (C) 2020 the Authors. Published by Elsevier B.V.

关键词： artificial intelligence deep learning computer vision automated programming collaborative work

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Progressive Scale Expansion Network with Octave Convolution for Arbitrary Shape Scene Text Detection 5th

Progressive Scale Expansion Network with Octave Convolution ...

引用

5th Asian conference on pattern recognition, ACPR 2019

作者： Yan, Shi Feng, Wei Zhao, Peng Liu, Cheng-Lin School of Computer Science and Technology Anhui University Hefei China Institute of Automation of Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9783030414030

Scene text detection is a challenging problem due to the image cluttering and high variability of text shape. Many methods have been proposed for multi-oriented and arbitrary shape text detection, in which the storage and computation costs of deep neural networks are still concerns. In this paper, we first introduce Octave Convolution into scene text detection for enlarging the receptive fields and reducing the spatial redundancy of networks. then we combine Octave Convolution with a state-of-the-art arbitrary shape text detector PSENet, which predicts different scale of kernels for each text instance. Experimental results on several benchmarks show that the proposed method can improve both detection performance and speed in detecting multi-oriented and arbitrary shape texts. Furthermore, our method achieves state-of-the-art performances on these benchmarks. © Springer Nature Switzerland AG 2020.

关键词： Convolution

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Overview on vision-Based 3D Object recognition Methods 1

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10th International conference on Image and Graphics (ICIG)

作者： Dong, Tianzhen Qi, Xiao Zhang, Qing Li, Wenju Xiong, Liang Shanghai Inst Technol Sch Comp Sci & Informat Engn Shanghai 201418 Peoples R China Shanghai Univ Finance & Econ Sch Humanities Shanghai 200433 Peoples R China

ISBN: (数字)9783030341107

ISBN: (纸本)9783030341107;9783030341091

In the fields of computer vision and pattern recognition, 3D object recognition has always been one of the most challenging problems, and has become an important direction of current image recognition research. this paper introduces the main methods of 3D object recognition and its key technologies comprehensively. It compares the advantages and disadvantages of various methods, and hopes to have a more comprehensive learning and grasp of 3D object recognition, and further clarify the future research direction.

关键词： computer vision 3D object recognition Overview

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PIPAL: A Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration 16th

PIPAL: A Large-Scale Image Quality Assessment Dataset for Pe...

引用

16th European conference on computer vision, ECCV 2020

作者： Jinjin, Gu Haoming, Cai Haoyu, Chen Xiaoxing, Ye Ren, Jimmy S. Chao, Dong The School of Data Science The Chinese University of Hong Kong Shenzhen China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China SenseTime Research Science Park Hong Kong SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9783030586201

Image quality assessment (IQA) is the key factor for the fast development of image restoration (IR) algorithms. the most recent IR methods based on Generative Adversarial Networks (GANs) have achieved significant improvement in visual performance, but also presented great challenges for quantitative evaluation. Notably, we observe an increasing inconsistency between perceptual quality and the evaluation results. then we raise two questions: (1) Can existing IQA methods objectively evaluate recent IR algorithms? (2) When focus on beating current benchmarks, are we getting better IR algorithms? To answer these questions and promote the development of IQA methods, we contribute a large-scale IQA dataset, called Perceptual Image Processing Algorithms (PIPAL) dataset. Especially, this dataset includes the results of GAN-based methods, which are missing in previous datasets. We collect more than 1.13 million human judgments to assign subjective scores for PIPAL images using the more reliable "Elo system". Based on PIPAL, we present new benchmarks for both IQA and super-resolution methods. Our results indicate that existing IQA methods cannot fairly evaluate GAN-based IR algorithms. While using appropriate evaluation methods is important, IQA methods should also be updated along with the development of IR algorithms. At last, we improve the performance of IQA networks on GAN-based distortions by introducing anti-aliasing pooling. Experiments show the effectiveness of the proposed method. © 2020, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Decoupling spatial pattern and its movement via complex factorization over orthogonal filter pairs 16

Decoupling spatial pattern and its movement via complex fact...

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16th conference on computer and Robot vision (CRV)

作者： Mu, Yanyan Dimitrakopoulos, Roussos Ferrie, Frank McGill Univ Ctr Intelligent Machines McConnell Engn BldgRoom 4313480 Univ St Montreal PQ H3A 2A7 Canada McGill Univ Dept Min & Mat Engn Adams BldgRoom 1073450 Univ St Montreal PQ H3A 2A7 Canada

ISBN: (纸本)9781728118383

Variations between related images (e.g. due to motions) can caused by different independent factors. A qualified representation can decouple the underlying explanatory factors rather than keeping them mixed. After decoupling, each factor lies in a lower dimension abstract space. Different computer vision tasks can be done in different abstract spaces more efficiently than in the original pixel space. For example, conducting object recognition in appearance space can result in an invariant recognition;estimating object motion in location space yields a result regardless of the object itself. In this paper, we propose an algorithm to decouple object appearance and location to amplitude and phase in static images by using complex factorization over orthogonal filter pairs. In particular, we show that, i) Orthogonal filter pairs can be learned in an unsupervised manner from multiple consecutive frames;ii) Object movement is encoded in the factorized phase gradient between frames over time. As a proof of concept, we present experiments on the application of our framework to the recovery of the optical flow. Here object movement is successfully captured by phase gradient.

关键词： representation learning complex factorization optical flow

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Towards Accurate Scene Text recognition With Semantic Reasoning Networks

Towards Accurate Scene Text Recognition With Semantic Reason...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Deli Yu Xuan Li Chengquan Zhang Tao Liu Junyu Han Jingtuo Liu Errui Ding School of Artificial Intelligence University of Chinese Academy of Sciences National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Department of Computer Vision Technology(VIS) Baidu Inc.

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Scene text image contains two levels of contents: visual texture and semantic information. Although the previous scene text recognition methods have made great progress over the past few years, the research on mining semantic information to assist text recognition attracts less attention, only RNN-like structures are explored to implicitly model semantic information. However, we observe that RNN based methods have some obvious shortcomings, such as time-dependent decoding manner and one-way serial transmission of semantic context, which greatly limit the help of semantic information and the computation efficiency. To mitigate these limitations, we propose a novel end-to-end trainable framework named semantic reasoning network (SRN) for accurate scene text recognition, where a global semantic reasoning module (GSRM) is introduced to capture global semantic context through multi-way parallel transmission. the state-of-the-art results on 7 public benchmarks, including regular text, irregular text and non-Latin long text, verify the effectiveness and robustness of the proposed method. In addition, the speed of SRN has significant advantages over the RNN based methods, demonstrating its value in practical use.

关键词： Semantics Visualization Text recognition Cognition Feature extraction Decoding Robustness

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Faster Person Re-identification 1

引用

16th European conference on computer vision, ECCV 2020

作者： Wang, Guan’an Gong, Shaogang Cheng, Jian Hou, Zengguang Beijing China Queen Mary University of London London United Kingdom School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China National Laboratory of Pattern Recognition Institute of Automation CAS Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology Beijing China

ISBN: (数字)9783030585983

ISBN: (纸本)9783030585976

Fast person re-identification (ReID) aims to search person images quickly and accurately. the main idea of recent fast ReID methods is the hashing algorithm, which learns compact binary codes and performs fast Hamming distance and counting sort. However, a very long code is needed for high accuracy (e.g. 2048), which compromises search speed. In this work, we introduce a new solution for fast ReID by formulating a novel Coarse-to-Fine (CtF) hashing code search strategy, which complementarily uses short and long codes, achieving both faster speed and better accuracy. It uses shorter codes to coarsely rank broad matching similarities and longer codes to refine only a few top candidates for more accurate instance ReID. Specifically, we design an All-in-One (AiO) framework together with a Distance threshold Optimization (DTO) algorithm. In AiO, we simultaneously learn and enhance multiple codes of different lengths in a single model. It learns multiple codes in a pyramid structure, and encourage shorter codes to mimic longer codes by self-distillation. DTO solves a complex threshold search problem by a simple optimization process, and the balance between accuracy and speed is easily controlled by a single parameter. It formulates the optimization target as a Fβ score that can be optimised by Gaussian cumulative distribution functions. Experimental results on 2 datasets show that our proposed method (CtF) is not only 8 % more accurate but also 5 × faster than contemporary hashing ReID methods. Compared with non-hashing ReID methods, CtF is 50 × faster with comparable accuracy. Code is available at https://***/wangguanan/light-reid. © 2020, Springer Nature Switzerland AG.

关键词： Hamming distance

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Optical Flow Assisted Monocular Visual Odometry 5th

Optical Flow Assisted Monocular Visual Odometry

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5th Asian conference on pattern recognition, ACPR 2019

作者： Wan, Yiming Gao, Wei Wu, Yihong NLPR Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9783030414030

this paper proposes a novel deep learning based approach for monocular visual odometry (VO) called FlowVO-Net. Our approach utilizes CNN to extract motion information between two consecutive frames and employs Bi-directional convolution LSTM (Bi-ConvLSTM) for temporal modelling. ConvLSTM can encode not only temporal information but also spatial correlation, and the bidirectional architecture enables it to learn the geometric relationship from image sequences pre and post. Besides, our approach jointly predicts optical flow as an auxiliary task in a self-supervised way by measuring photometric consistency. Experiment results indicate competitive performance of the proposed FlowVO-Net to the state-of-art methods. © Springer Nature Switzerland AG 2020.

关键词： Optical flows

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