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arXiv 2022年

作者： Chen, Boyu Qiao, Yu Wang, Yali ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Shanghai AI Laboratory Shanghai China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China

Tiny Actions Challenge focuses on understanding human activities in real-world surveillance. Basically, there are two main difficulties for activity recognition in this scenario. First, human activities are often recorded at a distance, and appear in a small resolution without much discriminative clue. Second, these activities are naturally distributed in a long-tailed way. It is hard to alleviate data bias for such heavy category imbalance. To tackle these problems, we propose a comprehensive recognition solution in this paper. First, we train video backbones with data balance, in order to alleviate overfitting in the challenge benchmark. Second, we design a dual-resolution distillation framework, which can effectively guide low-resolution action recognition by super-resolution knowledge. Finally, we apply model ensemble with post-processing, which can further boost performance on the long-tailed categories. Our solution ranks Top-1 on the leaderboard. Copyright © 2022, The Authors. All rights reserved.

关键词： Distillation

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Special Issue on Face Presentation Attack Detection

IEEE Transactions on Biometrics, Behavior, and Identity Scie...

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IEEE Transactions on Biometrics, Behavior, and Identity Science 2021年第3期3卷 282-284页

作者： Wan, Jun Escalera, Sergio Escalante, Hugo Jair Guo, Guodong Li, Stan Z. National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China Computer Vision Center Universitat de Barcelona Barcelona08007 Spain Instituto Nacional de Astrofísica Óptica y Electrónica Puebla72840 Mexico Institute of Deep Learning Baidu Research Beijing100193 China Center for Ai Research and Innovation Westlake University Hangzhou310024 China

Face presentation attack detection, also termed Face Anti-Spoofing (FAS) [item 1), 2) in the Appendix), is a hot and challenging research topic that has received much attention from the computer vision and pattern recognition communities in the past. Owing to the development of deep learning and big data, recent advances in this and related fields has increased considerably. However, there are still several challenging tasks that deserve attention from the community, for instance robust techniques to unknown spoofing attacks, cross-domain generalization, and multi-modal fusion in images and video sequences. We edited this special issue with the goal of compiling the latest progress in the field and identifying promising research opportunities on FAS. © 2019 IEEE.

关键词： Special issues and sections Forgery Information integrity Face recognition Streaming media Feature extraction

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AGA-GAN: Attribute guided attention generative adversarial network with U-net for face hallucination

arXiv

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arXiv 2021年

作者： Srivastava, Abhishek Chanda, Sukalpa Pal, Umapada Computer Vision and Pattern Recognition Unit Indian Statistical Institute West Bengal Kolkata700108 India Department of Computer Science and Communication Østfold University College Halden Norway

The performance of facial super-resolution methods relies on their ability to recover facial structures and salient features effectively. Even though the convolutional neural network and generative adversarial network-based methods deliver impressive performances on face hallucination tasks, the ability to use attributes associated with the low-resolution images to improve performance is unsatisfactory. In this paper, we propose an Attribute Guided Attention Generative Adversarial Network which employs novel attribute guided attention (AGA) modules to identify and focus the generation process on various facial features in the image. Stacking multiple AGA modules enables the recovery of both high and low-level facial structures. We design the discriminator to learn discriminative features exploiting the relationship between the high-resolution image and their corresponding facial attribute annotations. We then explore the use of U-Net based architecture to refine existing predictions and synthesize further facial details. Extensive experiments across several metrics show that our AGA-GAN and AGA-GAN+U-Net framework outperforms several other cutting-edge face hallucination state-of-the-art methods. We also demonstrate the viability of our method when every attribute descriptor is not known and thus, establishing its application in real-world scenarios. © 2021, CC BY.

关键词： Generative adversarial networks

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An Hybrid Attention-Based System for the Prediction of Facial Attributes 4th

An Hybrid Attention-Based System for the Prediction of Fac...

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4th International Workshop on Brain-Inspired Computing, BrainComp 2019

作者： Khellat-Kihel, Souad Sun, Zhenan Tistarelli, Massimo Computer Vision Laboratory University of Sassari Viale Italia 39 Sassari07100 Italy Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Room 1605 Intelligence Bulding 95 Zhongguancun East Road Beijing100190 China Computer Vision Laboratory Department of Biomedical Sciences and Information Technology University of Sassari Viale S. Pietro 43/b Sassari07100 Italy

ISBN: (纸本)9783030824266

Recent research on face analysis has demonstrated the richness of information embedded in feature vectors extracted from a deep convolutional neural network. Even though deep learning achieved a very high performance on several challenging visual tasks, such as determining the identity, age, gender and race, it still lacks a well grounded theory which allows to properly understand the processes taking place inside the network layers. Therefore, most of the underlying processes are unknown and not easy to control. On the other hand, the human visual system follows a well understood process in analyzing a scene or an object, such as a face. The direction of the eye gaze is repeatedly directed, through purposively planned saccadic movements, towards salient regions to capture several details. In this paper we propose to capitalize on the knowledge of the saccadic human visual processes to design a system to predict facial attributes embedding a biologically-inspired network architecture, the HMAX. The architecture is tailored to predict attributes with different textural information and conveying different semantic meaning, such as attributes related and unrelated to the subject’s identity. Salient points on the face are extracted from the outputs of the S2 layer of the HMAX architecture and fed to a local texture characterization module based on LBP (Local Binary pattern). The resulting feature vector is used to perform a binary classification on a set of pre-defined visual attributes. The devised system allows to distill a very informative, yet robust, representation of the imaged faces, allowing to obtain high performance but with a much simpler architecture as compared to a deep convolutional neural network. Several experiments performed on publicly available, challenging, large datasets demonstrate the validity of the proposed approach. © 2021, The Author(s).

关键词： Network architecture

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A New Defect Detection Method for Improving Text Detection and recognition Performances in Natural Scene Images

A New Defect Detection Method for Improving Text Detection a...

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2020 Swedish Workshop on Data Science, SweDS 2020

作者： Mokayed, Hamam Shivakumara, Palaiahnakote Liwicki, Marcus Pal, Umapada University of Malaya Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia Lulea University of Technology Department of Computer Science Electrical and Space Engineering Sweden Indian Statistical Institute Computer Vision and Pattern Recognition Unit Kolkata India

ISBN: (纸本)9781728192048

This paper presents a new idea for improving text detection and recognition performances by detecting defects in the text detection results. Despite the rapid development of powerful deep learning based models for scene text detection and recognition in the wild, in complex situations (logos or decorated components connected with text), existing methods do not yield satisfactory results. In this paper, we propose to use post-processing method to improve the text detection and recognition performance. The proposed method extracts features, namely phase congruency, entropy and compactness for the text detection results. To strengthen discriminative power for feature extraction, we explore the combination of SVM classifier and Gaussian distribution of text components to determine proper weight, which represents true text component. The weights are multiplied with the features to detect defect components though clustering. The bounding boxes are redrawn, which results proper bounding box without defects components. Experimental results show that the proposed defect detection reports satisfactory results. To validate the effectiveness of defect detection, we conduct experiments on benchmark datasets of MSRA-TD-500 and SVT for detection and recognition before and after defect detection. The result shows that the performance of text detection and recognition improves significantly after defect detection. © 2020 IEEE.

关键词： Gaussian distribution

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A method to generate synthetically warped document image 4th

A method to generate synthetically warped document image

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4th International Conference on computer vision and Image Processing, CVIP 2019

作者： Garai, Arpan Biswas, Samit Mandal, Sekhar Chaudhuri, Bidyut B. Department of Computer Science and Technology Indian Institute of Engineering Sciences and Technology Shibpur HowrahWest Bengal711103 India Techno India University Kolkata India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9789811540141

The digital camera captured document images may often be warped and distorted due to different camera angles or document surfaces. A robust technique is needed to solve this kind of distortion. The research on dewarping of the document suffers due to the limited availability of benchmark public dataset. In recent times, deep learning based approaches are used to solve the problems accurately. To train most of the deep neural networks a large number of document images is required and generating such a large volume of document images manually is difficult. In this paper, we propose a technique to generate a synthetic warped image from a flat-bedded scanned document image. It is done by calculating warping factors for each pixel position using two warping position parameters (WPP) and eight warping control parameters (WCP). These parameters can be specified as needed depending upon the desired warping. The results are compared with similar real captured images both qualitative and quantitative way. © Springer Nature Singapore Pte Ltd 2020.

关键词： Deep neural networks

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CoCoNet: A Collaborative Convolutional Network applied to fine-grained bird species classification

CoCoNet: A Collaborative Convolutional Network applied to fi...

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International Conference on Image and vision Computing New Zealand, IVCNZ

作者： Tapabrata Chakraborti Brendan McCane Steven Mills Umapada Pal University of Otago Computer Vision and Pattern recognition Unit Indian Statistical Institute

The following topics are dealt with: convolutional neural nets; learning (artificial intelligence); image classification; computer vision; feature extraction; video signal processing; deep learning (artificial intelli... 详细信息

ISBN: (数字)9781728185798

ISBN: (纸本)9781728185804

关键词： Training Visualization Image recognition Collaboration computer architecture Birds Task analysis

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Recognizing Bengali Word Images - A Zero-Shot Learning Perspective

Recognizing Bengali Word Images - A Zero-Shot Learning Persp...

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International Conference on pattern recognition

作者： Sukalpa Chanda Daniël Haitink Prashant Kumar Prasad Jochem Baas Umapada Pal Lambert Schomaker Østfold University College Norway Faculty of Science and Engineering. Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen The Netherlands Computer Vision and Pattern Recognition Unit Indian Statistical Institute India

Zero-Shot Learning(ZSL) techniques could classify a completely unseen class, which it has never seen before during training. Thus, making it more apt for any real-life classification problem, where it is not possible to train a system with annotated data for all possible class types. This work investigates recognition of word images written in Bengali Script in a ZSL framework. The proposed approach performs Zero-Shot word recognition by coupling deep learned features procured from various CNN architectures along with 13 basic shapes/stroke primitives commonly observed in Bengali script characters. As per the notion of ZSL framework those 13 basic shapes are termed as “Signature/Semantic Attributes”. The obtained results are promising while evaluation was carried out in a Five-Fold cross-validation setup dealing with samples from 250 word classes.

关键词： Training Couplings Image recognition Shape Character recognition

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Activating More Pixels in Image Super-Resolution Transformer

arXiv

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arXiv 2022年

作者： Chen, Xiangyu Wang, Xintao Zhou, Jiantao Qiao, Yu Dong, Chao State Key Laboratory of Internet of Things for Smart City University of Macau China Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China Shanghai Artificial Intelligence Laboratory China ARC Lab Tencent PCG China

Transformer-based methods have shown impressive performance in low-level vision tasks, such as image super-resolution. However, we find that these networks can only utilize a limited spatial range of input information through attribution analysis. This implies that the potential of Transformer is still not fully exploited in existing networks. In order to activate more input pixels for better reconstruction, we propose a novel Hybrid Attention Transformer (HAT). It combines both channel attention and window-based self-attention schemes, thus making use of their complementary advantages of being able to utilize global statistics and strong local fitting capability. Moreover, to better aggregate the cross-window information, we introduce an overlapping cross-attention module to enhance the interaction between neighboring window features. In the training stage, we additionally adopt a same-task pre-training strategy to exploit the potential of the model for further improvement. Extensive experiments show the effectiveness of the proposed modules, and we further scale up the model to demonstrate that the performance of this task can be greatly improved. Our overall method significantly outperforms the state-of-the-art methods by more than 1dB. Copyright © 2022, The Authors. All rights reserved.

关键词： Pixels

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NTIRE 2023 Image Shadow Removal Challenge Report

NTIRE 2023 Image Shadow Removal Challenge Report

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2023 IEEE/CVF Conference on computer vision and pattern recognition Workshops, CVPRW 2023

作者： Vasluianu, Florin-Alexandru Seizinger, Tim Timofte, Radu Cui, Shuhao Huang, Junshi Tian, Shuman Fan, Mingyuan Zhang, Jiaqi Zhu, Li Wei, Xiaoming Wei, Xiaolin Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Dong, Xiaoyi Zhang, Xi Sheryl Li, Chenghua Leng, Cong Yeo, Woon-Ha Oh, Wang-Taek Lee, Yeo-Reum Ryu, Han-Cheol Luo, Jinting Jiang, Chengzhi Han, Mingyan Wu, Qi Lin, Wenjie Yu, Lei Li, Xinpeng Jiang, Ting Fan, Haoqiang Liu, Shuaicheng Xu, Shuning Song, Binbin Chen, Xiangyu Zhang, Shile Zhou, Jiantao Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Wang, Bo Ren, Jiahuan Luo, Yan Kondo, Yuki Miyata, Riku Yasue, Fuma Naruki, Taito Ukita, Norimichi Chang, Hua-En Yang, Hao-Hsiang Chen, Yi-Chung Chiang, Yuan-Chun Huang, Zhi-Kai Chen, Wei-Ting Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Xianwei, Li Fu, Huiyuan Liu, Chunlin Ma, Huadong Fu, Binglan He, Huiming Wang, Mengjia She, Wenxuan Liu, Yu Nathan, Sabari Kansal, Priya Zhang, Zhongjian Yang, Huabin Wang, Yan Zhang, Yanru Phutke, Shruti S. Kulkarni, Ashutosh Khan, Md Raqib Murala, Subrahmanyam Vipparthi, Santosh Kumar Ye, Heng Liu, Zixi Yang, Xingyi Liu, Songhua Wu, Yinwei Jing, Yongcheng Yu, Qianhao Zheng, Naishan Huang, Jie Long, Yuhang Yao, Mingde Zhao, Feng Zhao, Bowen Ye, Nan Shen, Ning Cao, Yanpeng Xiong, Tong Xia, Weiran Li, Dingwen Xia, Shuchen Computer Vision Lab Ifi Caidas University of Würzburg Germany Computer Vision Lab Eth Zürich Switzerland Meituan Group China Department of Information Technology Uppsala University Sweden Institute of Automation Chinese Academy of Sciences Beijing China Nanjing China Maicro Nanjing China Department of Artificial Intelligence Convergence Sahmyook University Seoul Korea Republic of Megvii Technology China University of Electronic Science and Technology of China China University of Macau China China Toyota Technological Institute Japan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States Beijing University of Post and Teleconmunication Beijing China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education China Couger Inc. Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar Punjab Rupnagar India Research Institute Singapore National University of Singapore Singapore Research Institute Singapore University of Sydney Australia Brain-Inspired Vision Laboratory Information Science and Technology Institution University of Science and Technology of China China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou310027 China South China University of Technology China

ISBN: (纸本)9798350302493

This work reviews the results of the NTIRE 2023 Challenge on Image Shadow Removal. The described set of solutions were proposed for a novel dataset, which captures a wide range of object-light interactions. It consists of 1200 roughly pixel aligned pairs of real shadow free and shadow affected images, captured in a controlled environment. The data was captured in a white-box setup, using professional equipment for lights and data acquisition sensors. The challenge had a number of 144 participants registered, out of which 19 teams were compared in the final ranking. The proposed solutions extend the work on shadow removal, improving over the performance level describing state-of-the-art methods. © 2023 IEEE.

关键词： Data acquisition

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