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arXiv 2020年

作者： Seungjun, Nah Sanghyun, Son Radu, Timofte Kyoung Mu, Lee Tseng, Yu Xu, Yu-Syuan Chiang, Cheng-Ming Tsai, Yi-Min Brehm, Stephan Scherer, Sebastian Xu, Dejia Chu, Yihao Sun, Qingyan Jiang, Jiaqin Duan, Lunhao Yao, Jian Purohit, Kuldeep Suin, Maitreya Rajagopalan, A.N. Ito, Yuichi Hrishikesh, P.S. Puthussery, Densen Akhil, K.A. Jiji, C.V. Kim, Guisik Deepa, P.L. Xiong, Zhiwei Huang, Jie Liu, Dong Kim, Sangmin Nam, Hyungjoon Kim, Jisu Jeong, Jechang Huang, Shihua Fan, Yuchen Yu, Jiahui Yu, Haichao Huang, Thomas S. Zhou, Ya Li, Xin Liu, Sen Chen, Zhibo Dutta, Saikat Das, Sourya Dipta Garg, Shivam Sprague, Daniel Patel, Bhrij Huck, Thomas Department of ECE ASRI SNU Korea Republic of Computer Vision Lab ETH Zurich Switzerland MediaTek Inc University of Augsburg Chair for Multimedia Computing and Computer Vision Lab Germany Peking University China Beijing University of Posts and Telecommunications China Beijing Jiaotong University China Wuhan University China Indian Institute of Technology Madras India Vermilion College of Engineering Trivandrum India CVML Chung-Ang University Korea Republic of APJ Abdul Kalam Technological University India University of Science and Technology of China China Image Communication Signal Processing Laboratory Hanyang University Korea Republic of Southern University of Science and Technology China University of Illinois at Urbana-Champaign United States CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China China IIT Madra Jadavpur University India University of Texas Austin United States Duke University Computer Science Department United States

Motion blur is one of the most common degradation artifacts in dynamic scene photography. This paper reviews the NTIRE 2020 Challenge on Image and Video Deblurring. In this challenge, we present the evaluation results from 3 competition tracks as well as the proposed solutions. Track 1 aims to develop single-image deblurring methods focusing on restoration quality. On Track 2, the image deblurring methods are executed on a mobile platform to find the balance of the running speed and the restoration accuracy. Track 3 targets developing video deblurring methods that exploit the temporal relation between input frames. In each competition, there were 163, 135, and 102 registered participants and in the final testing phase, 9, 4, and 7 teams competed. The winning methods demonstrate the state-of-the-art performance on image and video deblurring tasks. Copyright © 2020, The Authors. All rights reserved.

关键词： Image enhancement

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WIDER face and pedestrian challenge 2018 methods and results

arXiv

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arXiv 2019年

作者： Loy, Chen Change Lin, Dahua Ouyang, Wanli Xiong, Yuanjun Yang, Shuo Huang, Qingqiu Zhou, Dongzhan Xia, Wei Li, Quanquan Luo, Ping Yan, Junjie Wang, Jianfeng Li, Zuoxin Yuan, Ye Li, Boxun Shao, Shuai Yu, Gang Wei, Fangyun Ming, Xiang Chen, Dong Zhang, Shifeng Chi, Cheng Lei, Zhen Li, Stan Z. Zhang, Hongkai Ma, Bingpeng Chang, Hong Shan, Shiguang Chen, Xilin Liu, Wu Zhou, Boyan Li, Huaxiong Cheng, Peng Mei, Tao Kukharenko, Artem Vasenin, Artem Sergievskiy, Nikolay Yang, Hua Li, Liangqi Xu, Qiling Hong, Yuan Chen, Lin Sun, Mingjun Mao, Yirong Luo, Shiying Li, Yongjun Wang, Ruiping Xie, Qiaokang Wu, Ziyang Lu, Lei Liu, Yiheng Zhou, Wengang Nanyang Technological University Singapore Singapore Chinese University of Hong Kong Amazon Web Services University of Sydney SenseTime Megvii Microsoft Research Asia University of Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences Jd Ai Research. Computer Vision and Multimedia Lab NtechLab Shanghai Jiao Tong University University of Science and Technology of China and Iflytek

This paper presents a review of the 2018 WIDER Challenge on Face and Pedestrian. The challenge focuses on the problem of precise localization of human faces and bodies, and accurate association of identities. It comprises of three tracks: (i) WIDER Face which aims at soliciting new approaches to advance the state-of-the-art in face detection, (ii) WIDER Pedestrian which aims to find effective and efficient approaches to address the problem of pedestrian detection in unconstrained environments, and (iii) WIDER Person Search which presents an exciting challenge of searching persons across 192 movies. In total, 73 teams made valid submissions to the challenge tracks. We summarize the winning solutions for all three tracks. and present discussions on open problems and potential research directions in these topics. Copyright © 2019, The Authors. All rights reserved.

关键词： Face recognition

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A closer look: Small object detection in faster R-CNN

A closer look: Small object detection in faster R-CNN

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IEEE International Conference on multimedia and Expo (ICME)

作者： Christian Eggert Stephan Brehm Anton Winschel Dan Zecha Rainer Lienhart Multimedia Computing and Computer Vision Lab University of Augsburg

ISBN: (纸本)9781509060689

Faster R-CNN is a well-known approach for object detection which combines the generation of region proposals and their classification into a single pipeline. In this paper we apply Faster R-CNN to the task of company logo detection. Motivated by the weak performance of Faster R-CNN on small object instances, we perform a detailed examination of both the proposal and the classification stage, examining their behavior for a wide range of object sizes. Additionally, we look at the influence of feature map resolution on the performance of those stages. We introduce an improved scheme for generating anchor proposals and propose a modification to Faster R-CNN which leverages higher-resolution feature maps for small objects. We evaluate our approach on the Flicker data set improving the detection performance on small object instances.

关键词： Proposals Companies Object detection Feature extraction Pipelines Image resolution Barium

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Explaining first impressions: Modeling, recognizing, and explaining apparent personality from videos

arXiv

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arXiv 2018年

作者： Escalante, Hugo Jair Kaya, Heysem Salah, Albert Ali Escalera, Sergio Güçlütürk, Yağmur Güçlü, Umut Baró, Xavier Guyon, Isabelle Jacques, Julio Madadi, Meysam Ayache, Stephane Viegas, Evelyne Gürpınar, Furkan Wicaksana, Achmadnoer Sukma Liem, Cynthia C.S. van Gerven, Marcel A.J. van Lier, Rob INAOE Mexico ChaLearn United States Namık Kemal University Department of Computer Engineering Turkey Bogaziçi University Dept. of Computer Engineering Turkey and Nagoya University FCVRC Japan Bogaziçi University Computational Science and Engineering Turkey University of Barcelona and Computer Vision Center Spain Computer Vision Center Spain Radboud University Donders Institute for Brain Cognition and Behaviour Nijmegen Netherlands Universitat Oberta de Catalunya and Computer Vision Center Spain UPSud/INRIA Université Paris-Saclay France Aix Marseille Univ CNRS LIF Marseille France Microsoft Research United States Multimedia Computing Group Delft University of Technology Delft Netherlands

Explainability and interpretability are two critical aspects of decision support systems. Within computer vision, they are critical in certain tasks related to human behavior analysis such as in health care applications. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in the context of computer vision with an emphasis on looking at people tasks. Specifically, we review and study those mechanisms in the context of first impressions analysis. To the best of our knowledge, this is the first effort in this direction. Additionally, we describe a challenge we organized on explainability in first impressions analysis from video. We analyze in detail the newly introduced data set, evaluation protocol, proposed solutions and summarize the results of the challenge. Finally, derived from our study, we outline research opportunities that we foresee will be decisive in the near future for the development of the explainable computer vision field. Copyright © 2018, The Authors. All rights reserved.

关键词： Decision support systems

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Saliency-guided selective magnification for company logo detection

Saliency-guided selective magnification for company logo det...

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International Conference on Pattern Recognition

作者： Christian Eggert Anton Winschel Dan Zecha Rainer Lienhart Multimedia Computing and Computer Vision Lab University of Augsburg Augsburg Germany

ISBN: (纸本)9781509048489

Fast R-CNN is a well-known approach to object detection which is generally reported to be robust to scale changes. In this paper we examine the influence of scale within the detection pipeline in the case of company logo detection. We demonstrate that Fast R-CNN encounters problems when handling objects which are significantly smaller than the receptive field of the utilized network. In order to overcome these difficulties, we propose a saliency-guided multiscale approach that does not rely on building a full image pyramid. We use the feature representation computed by Fast R-CNN to directly classify large objects while at the same time predicting salient regions which contain small objects with high probability. Only selected regions are magnified and a new feature representation for these enlarged regions is calculated. Feature representations from both scales are used for classification, improving the detection quality of small objects while keeping the computational overhead low. Compared to a naive magnification strategy we are able to retain 79% of the performance gain while only spending 36% of the computation time.

关键词： Proposals Object detection Companies Feature extraction Neurons Support vector machines Neural networks

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Norm-Induced Entropies for Decision Forests

Norm-Induced Entropies for Decision Forests

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IEEE Workshop on Applications of computer vision (WACV)

作者： Christoph Lassner Rainer Lienhart Multimedia Computing and Computer Vision Lab University of Augsburg

ISBN: (纸本)9781479966844

The entropy measurement function is a central element of decision forest induction. The Shannon entropy and other generalized entropies such as the Renyi and Tsallis entropy are designed to fulfill the Khinchin-Shannon axioms. Whereas these axioms are appropriate for physical systems, they do not necessarily model well the artificial system of decision forest induction. In this paper, we show that when omitting two of the four axioms, every norm induces an entropy function. The remaining two axioms are sufficient to describe the requirements for an entropy function in the decision forest context. Furthermore, we introduce and analyze the p-norm-induced entropy, show relations to existing entropies and the relation to various heuristics that are commonly used for decision forest training. In experiments with classification, regression and the recently introduced Hough forests, we show how the discrete and differential form of the new entropy can be used for forest induction and how the functions can simply be fine tuned. The experiments indicate that the impact of the entropy function is limited, however can be a simple and useful post-processing step for optimizing decision forests for high performance applications.

关键词： Entropy Training Context Vegetation Vectors computer vision Equations

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Key-Pose Prediction in Cyclic Human Motion

Key-Pose Prediction in Cyclic Human Motion

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IEEE Workshop on Applications of computer vision (WACV)

作者： Dan Zecha Rainer Lienhart Multimedia Computing and Computer Vision Lab University of Augsburg

In this paper we study the problem of estimating inner cyclic time intervals within repetitive motion sequences of top-class swimmers in a swimming channel. Interval limits are given by temporal occurrences of key-poses, i.e. distinctive postures of the body. A key-pose is defined by means of only one or two specific features of the complete posture. It is often difficult to detect such subtle features directly. We therefore propose the following method: Given that we observe the swimmer from the side, we build a pictorial structure of pose lets to robustly identify random support poses within the regular motion of a swimmer. We formulate a maximum likelihood model which predicts a key-pose given the occurrences of multiple support poses within one stroke. The maximum likelihood can be extended with prior knowledge about the temporal location of a key-pose in order to improve the prediction recall. We experimentally show that our models reliably and robustly detect key-poses with a high precision and that their performance can be improved by extending the framework with additional camera views.

关键词： Detectors Time series analysis Joints Mathematical model Cameras Equations Feature extraction

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Fisher vector encoding of micro color features for (real world) jigsaw puzzles

Fisher vector encoding of micro color features for (real wor...

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International Conference on Document Analysis and Recognition

作者： Fabian Richter Christian Eggert Rainer Lienhart Multimedia Computing and Computer Vision Lab University of Augsburg Germany

In this work we propose a computationally efficient yet effective color encoding scheme for the purpose of jigsaw puzzle solving. Our contribution is twofold: (i) First of all, we show that a Fisher vector encoding gives superior performance over the commonly used compatibility descriptor of stacked color values. (ii) Furthermore, we show experimentally on synthetic and real-world data that our proposed color encoding is more robust in the presence of noise, as compared to three widely used features. Due to the robustness of our proposed descriptor we also anticipate its use to yield performance improvements in other applications, e.g., for the virtual reconstruction of hand-torn documents and archaeological findings.

关键词： Mixture models World Wide Web Silicon computers

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Color Object Recognition via Cross-Domain Learning on RGB-D Images

Color Object Recognition via Cross-Domain Learning on RGB-D ...

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IEEE International Conference on Robotics and Automation

作者： Yawen Huang Fan Zhu Ling Shao Alejandro F. Frangi the Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB) Department of Electronic and Electrical Engineering The University of Sheffield Sheffield United Kingdom the Multimedia and Visual Computing Lab Department of Electrical and Computer Engineering New York University Abu Dhabi Abu Dhabi the Computer Vision and Artificial Intelligence Group Department of Computer Science and Digital Technologies Northumbria University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9781467380270

This paper addresses the object recognition problem using multiple-domain inputs. We present a novel approach that utilizes labeled RGB-D data in the training stage, where depth features are extracted for enhancing the discriminative capability of the original learning system that only relies on RGB images. The highly dissimilar source and target domain data are mapped into a unified feature space through transfer at both feature and classifier levels. In order to alleviate cross-domain discrepancy, we employ a state-of-the-art domain-adaptive dictionary learning algorithm that updates image representations in both domains and the classifier parameters simultaneously. The proposed method is trained on a RGB-D Object dataset and evaluated on the Caltech-256 dataset. Experimental results suggest that our approach can lead to significant performance gain over the state-of-the-art methods.

关键词： RGB-D enhancing discriminative capability

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Improving VLAD: Hierarchical coding and a refined local coordinate system

Improving VLAD: Hierarchical coding and a refined local coor...

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作者： Eggert, Christian Romberg, Stefan Lienhart, Rainer Multimedia Computing and Computer Vision Lab University of Augsburg Germany

ISBN: (纸本)9781479957514

The enormous growth of image databases calls for new techniques for fast and effective image search that scales with millions of images. Most importantly, the setting requires a compact but also descriptive image signature. Recently, the vector of aggregated local descriptors (VLAD) [1] has received much attention in large-scale image retrieval. In this paper we present two modifications for VLAD which improve the retrieval performance of the signature. © 2014 IEEE.

关键词： Image retrieval

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