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2020 IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Shuang Li Jiaxi Jiang Philipp Ruppel Hongzhuo Liang Xiaojian Ma Norman Hendrich Fuchun Sun Jianwei Zhang TAMS (Technical Aspects of Multimodal Systems) Universität Hamburg RWTH Aachen University Center for Vision Cognition Learning and Autonomy University of California Los Angeles Beijing National Research Center for Information Science and Technology (BNRist) State Key Lab on Intelligent Technology and Systems Tsinghua University

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

In this paper, we present a multimodal mobile teleoperation system that consists of a novel vision-based hand pose regression network (Transteleop) and an IMU (inertial measurement units)-based arm tracking method. Transteleop observes the human hand through a low-cost depth camera and generates not only joint angles but also depth images of paired robot hand poses through an image-to-image translation process. A keypoint-based reconstruction loss explores the resemblance in appearance and anatomy between human and robotic hands and enriches the local features of reconstructed images. A wearable camera holder enables simultaneous hand-arm control and facilitates the mobility of the whole teleoperation system. Network evaluation results on a test dataset and a variety of complex manipulation tasks that go beyond simple pick-and-place operations show the efficiency and stability of our multimodal teleoperation system.

关键词： Measurement units Machine vision Robot vision systems Cameras Mobile robots Task analysis Image reconstruction

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Open-world Weakly-Supervised Object Localization

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Pattern Recognition 2026年 169卷

作者： Jinheng Xie Zhaochuan Luo Rouyi Li Yawen Huang Haozhe Liu Yuexiang Li Yefeng Zheng Yang Zhang Linlin Shen Mike Zheng Shou Computer Vision Institute School of Artificial Intelligence Shenzhen University Shenzhen 518060 China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen 518060 China The Jarvis Research Center Tencent YouTu Lab Shenzhen 518057 China National University of Singapore Singapore 639798 Singapore Shenzhen Power Supply Bureau Co. Ltd. ShenZhen 518000 China

While remarkable success has been achieved in weakly supervised object localization (WSOL), current frameworks are not capable of locating objects of novel categories in open-world settings. To address this issue, we are the first to introduce a new weakly supervised object localization task called Open-world Weakly Supervised Object Localization ( OWSOL ). During training, all labeled data comes from known categories and, both known and novel categories exist in the unlabeled data. To handle such data, we propose a novel paradigm of contrastive representation co-learning using both labeled and unlabeled data to perform Generalized Class Activation Mapping ( GCAM ) for object localization, without the requirement of bounding box annotation. As no class label is available for the unlabeled data, we conduct clustering over the full training set and design a novel multiple semantic centroids-driven contrastive loss for representation learning. We re-organize two widely used datasets, i.e., ImageNet-1K and iNatLoc500, and propose OpenImages150 to serve as evaluation benchmarks for OWSOL. Extensive experiments demonstrate that the proposed method can surpass all baselines by a large margin. We believe that this work can shift the close-set localization towards the open-world setting and serve as a foundation for subsequent works. The code and dataset are available at https://***/ryylcc/OWSOL .

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The quaternion-based anisotropic gradient for the color images 17

The quaternion-based anisotropic gradient for the color imag...

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17th Image Processing: Algorithms and systems Conference, IPAS 2019

作者： Voronin, V. Zelensky, A. Agaian, S. Don State Technical University Lab. «Mathematical Methods of Image Processing and Computer Vision Intelligent Systems» Gagarina 1 Rostov on Don Russia Moscow State University of Technology"STANKIN" Moscow Russia CUNY College of Staten Island Dept. of Computer Science New York United States

Image gradient, as a preprocessing step is an essential tool in image processing in many research areas such as edge detection, segmentation, smoothing, inpainting, etc. In the present paper, we develop a new gradient by integrated the quaternion framework with LPA-ICI (local polynomial approximation - the intersection of confidence intervals) based on anisotropic gradient concepts for the color image processing applications. computer simulations on the Berkeley Segmentation Dataset show that the new quaternion anisotropic gradient exhibits fewer color artifacts compared to state-of-the-art techniques including well-knowledge gradient operators. © 2019, Society for Imaging Science and Technology.

关键词： Polynomial approximation

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Feedback alfa-rooting algorithm for medical image enhancement 17

Feedback alfa-rooting algorithm for medical image enhancemen...

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17th Image Processing: Algorithms and systems Conference, IPAS 2019

作者： Voronin, V. Zelensky, A. Agaian, S. Don State Technical University Lab. «Mathematical Methods of Image Processing and Computer Vision Intelligent Systems» Gagarina 1 Rostov on Don Russia Moscow State University of Technology "STANKIN" Moscow Russia CUNY/ College of Staten Island Dept. of Computer Science New York United States

This paper presents a new combined local and global transform domain-based feedback image enhancement algorithm for medical diagnosis, treatment, and clinical research. The basic idea in using local alfa-rooting method is to apply it to different disjoint blocks with different sizes. The block size and alfa-rooting parameters driven through optimization using the Agaian's cost function (image enhancement non-reference quality measure). The presented new approach allows enhancing MRI and CT images with uneven lighting and brightness gradient by preserving the local image features/details. Extensive computer simulations (CS) on real medical images are offered to gage the presented method. CS shows that our method improves the contrast and enhances the details of the medical images effectively compared with the current state-of-art methods. © 2019, Society for Imaging Science and Technology.

关键词： Clinical research

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Language generation with multi-hop reasoning on commonsense knowledge graph

arXiv

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arXiv 2020年

作者： Ji, Haozhe Ke, Pei Huang, Shaohan Wei, Furu Zhu, Xiaoyan Huang, Minlie Department of Computer Science and Technology Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Microsoft Research United States

Despite the success of generative pre-trained language models on a series of text generation tasks, they still suffer in cases where reasoning over underlying commonsense knowledge is required during generation. Existing approaches that integrate commonsense knowledge into generative pre-trained language models simply transfer relational knowledge by post-training on individual knowledge triples while ignoring rich connections within the knowledge graph. We argue that exploiting both the structural and semantic information of the knowledge graph facilitates commonsense-aware text generation. In this paper, we propose Generation with Multi-Hop Reasoning Flow (GRF) that enables pre-trained models with dynamic multi-hop reasoning on multi-relational paths extracted from the external commonsense knowledge graph. We empirically show that our model outperforms existing baselines on three text generation tasks that require reasoning over commonsense knowledge. We also demonstrate the effectiveness of the dynamic multi-hop reasoning module with reasoning paths inferred by the model that provide rationale to the generation.1 Copyright © 2020, The Authors. All rights reserved.

关键词： Knowledge graph

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Image reconstruction using the modified texture synthesis algorithm

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IOP Conference Series: Materials Science and Engineering 2021年第1期1029卷

作者： R R Ibadov N V Gapon S R Ibadov S V Kucheryavenko Southern Federal University Taganrog Russia Lab. 'Mathematical methods of image processing and intelligent computer vision systems' Don State Technical University Rostov-on-Don Russia

The article discusses a method for image reconstruction based on the search for similar blocks using a texture synthesis algorithm. The effectiveness of the new approach is shown using several examples for various areas with lost pixels. The subject of the research is methods and algorithms for processing space-time reconstruction of two-dimensional signals based on a geometric model of images. The object of research is a set of test static images. The result of the study is a modification of the image reconstruction method based on the search for similar blocks in order to reduce the error in image reconstruction. The novelty of the work is an algorithm that improves the quality of image restoration. The results obtained make it possible to reduce the root mean square error.

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Abnormal Data Cleaning for Wind Turbines by Image Segmentation Based on Active Shape Model and Class Uncertainty

SSRN

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SSRN 2022年

作者： Liang, Guoyuan Su, Yahao Wu, Xinyu Ma, Jiajun Long, Huan Song, Zhe Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China School of Electrical Engineering Southeast University Jiang Su Province Nanjing China School of Business Nanjing University Jiang Su Province Nanjing China

Wind power curve describes the relationship between wind speed and output power of wind turbine, which may be contaminated due to various unexpected factors. Following the idea of image segmentation in our previous work, in this paper, we propose a novel image-based abnormal data cleaning algorithm aiming to provide high quality data for wind power curve modeling and relevant applications. For image binarization, a multiscale space is built to determine the optimal scale at which the most stable as well as informative shape representations are extracted. The Active Shape Model is utilized to model the power curve shape and generate a set of segmentation candidates for filtering abnormal data. To measure the cost of each candidate, the Class Uncertainty map based on dual feature images is built. And eventually the optimal segmentation boundary is determined by the optimization in the energy space. A series of comparative experiments conducted over the real-world datasets of 37 wind turbines have validated the superior performance of the proposed algorithm. © 2022, The Authors. All rights reserved.

关键词： Wind power

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CACAI: Comparative analysis of crypto apps in India

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AIP Conference Proceedings 2023年第1期2916卷

作者： Rahul Johari Anuj Talwar Suhas Harbola Jyotsna Yadav Himesh Nayak Deo Prakash Vidyarthi SWINGER: Security Wireless IoT Network Group of Engineering and Research University School of Information Communication and Technology (USICT) Guru Gobind Singh Indraprastha University Sector-16C Dwarka Delhi India Computer vision and Image processing lab University School of Information Communication and Technology (USICT) Guru Gobind Singh Indraprastha University Sector-16C Dwarka Delhi India School of Computer and Systems Sciences Parallel and Distributed Systems Lab JNU Delhi India

Crypto-currency is a decentralized digital currency which can be used as a medium of exchange. As it does not have any central authority, it is unregulated and a volatile asset. Globally the crypto market is worth a few trillion dollars. The Indian crypto market suffered a lot due to a ban imposed by the Reserve Bank of India (RBI), an Indian regulatory agency, which lasted for around two years. Even after the ban and cautions issued by the RBI, the crypto market in India touched a billion dollar mark in no time. Currently, the crypto market has more than 10 million Indian investors which is growing fast as the number of exchange apps in the Indian market increase with the ease of trading in crypto. In the current research work Comparative Analysis of Crypto App in India (CACAI) has been performed and results exceed the expectations.

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TR-BERT: Dynamic token reduction for accelerating BERT inference

arXiv

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arXiv 2021年

作者： Ye, Deming Lin, Yankai Huang, Yufei Sun, Maosong Department of Computer Science and Technology Tsinghua University Beijing China Institute for Artificial Intelligence Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology China State Key Lab on Intelligent Technology and Systems Tsinghua University Beijing China Beijing Academy of Artificial Intelligence China Pattern Recognition Center WeChat AI Tencent Inc

Existing pre-trained language models (PLMs) are often computationally expensive in inference, making them impractical in various resource-limited real-world applications. To address this issue, we propose a dynamic token reduction approach to accelerate PLMs' inference, named TR-BERT, which could flexibly adapt the layer number of each token in inference to avoid redundant calculation. Specially, TR-BERT formulates the token reduction process as a multi-step token selection problem and automatically learns the selection strategy via reinforcement learning. The experimental results on several downstream NLP tasks show that TR-BERT is able to speed up BERT by 2-5 times to satisfy various performance demands. Moreover, TR-BERT can also achieve better performance with less computation in a suite of long-text tasks since its token-level layer number adaption greatly accelerates the self-attention operation in PLMs. The source code and experiment details of this paper can be obtained from https://***/thunlp/TR-BERT. Copyright © 2021, The Authors. All rights reserved.

关键词： Reinforcement learning

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Improvement of the Human Action Recognition Algorithm by the Pre-processing of Input Data

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IOP Conference Series: Materials Science and Engineering 2021年第1期1029卷

作者： M M Zhdanova O S Balabaeva V V Voronin E A Semenishchev A A Zelensky Center for Cognitive Technology and Machine Vision Moscow State University of Technology «STANKIN» Moscow Russia Lab. «Mathematical methods of image processing and intelligent computer vision systems» Don State Technical University Rostov-on-Don Russia

The paper presents an approach to recognizing human actions using an additional preprocessing stage of input data. The growing volumes of video information do not always allow support the quality of data at a high level; this can cause limitations in the further processing of digital data. In this regard, it becomes urgent to introduce an additional stage of image enhancement into the algorithm for recognizing actions in video. The proposed method includes three main steps: image enhancement, constructing a descriptor, and classification. The presented image enhancement stage is based on the combined local and global image processing in the frequency domain. The basic idea in using local alfa-rooting method is to apply it to different disjoint blocks with different sizes. To solve the problem of constructing a descriptor, a three-dimensional microblock dense difference (3D DMD) algorithm is used, which provides a highly oriented representation of image regions by tightly capturing microblocks within each region in several orientations and scales. 3D DMD has several advantages over other methods: higher efficiency compared to existing methods; minimal computational costs when using an integrated image; low dimension; ease of implementation; does not require settings. The presented modification allows to increase productivity by 2-4%.

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