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arXiv 2019年

作者： Mo, Hanlin Li, Hua Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

In this paper, we design two fundamental differential operators for the derivation of rotation differential invariants of images. Each differential invariant obtained by using the new method can be expressed as a homogeneous polynomial of image partial derivatives, which preserve their values when the image is rotated by arbitrary angles. We produce all possible instances of homogeneous invariants up to the given order and degree, and discuss the independence of them in detail. As far as we know, no previous papers have published so many explicit forms of high-order rotation differential invariants of images. In the experimental part, texture classification and image patch verification are carried out on popular real databases. These rotation differential invariants are used as image feature vector. We mainly evaluate the effects of various factors on the performance of them. The experimental results also validate that they have better performance than some commonly used image features in some cases. Copyright © 2019, The Authors. All rights reserved.

关键词： Rotation

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Predicting compositional time series via autoregressive Dirichlet estimation

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中国科学（信息科学） 2018年第9期61卷 268-270页

作者： Ganbin ZHOU Ping LUO Qing HE Key Lab of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China

In recent years,compositional time series (CTS) prediction has become a widely applied data analysis method for modeling tactile sequence data [1],hydrological time series data using a four-stage algorithm (denoising,decomposition,components prediction and ensemble) [2],and daily and monthly extreme temperature data [3,4].

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DPANet: Depth potentiality-aware gated attention network for RGB-D salient object detection

arXiv

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arXiv 2020年

作者： Chen, Zuyao Cong, Runmin Xu, Qianqian Huang, Qingming The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100190 China The Institute of Information Science Beijing Jiaotong University Beijing100044 China The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The Department of Computer Science City University of Hong Kong Hong Kong Hong Kong The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing101408 China Peng Cheng Laboratory Shenzhen518055 China

There are two main issues in RGB-D salient object detection: (1) how to effectively integrate the complementarity from the cross-modal RGB-D data;(2) how to prevent the contamination effect from the unreliable depth map. In fact, these two problems are linked and intertwined, but the previous methods tend to focus only on the first problem and ignore the consideration of depth map quality, which may yield the model fall into the sub-optimal state. In this paper, we address these two issues in a holistic model synergistically, and propose a novel network named DPANet to explicitly model the potentiality of the depth map and effectively integrate the cross-modal complementarity. By introducing the depth potentiality perception, the network can perceive the potentiality of depth information in a learning-based manner, and guide the fusion process of two modal data to prevent the contamination occurred. The gated multi-modality attention module in the fusion process exploits the attention mechanism with a gate controller to capture long-range dependencies from a cross-modal perspective. Experimental results compared with 16 state-of-the-art methods on 8 datasets demonstrate the validity of the proposed approach both quantitatively and qualitatively. Copyright © 2020, The Authors. All rights reserved.

关键词： Modal analysis

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Correction to: Parallel optimization of the ray-tracing algorithm based on the HPM model

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The Journal of Supercomputing 2021年第9期77卷 10333-10333页

作者： Jun-Feng, Wang Gang-Yi, Ding Yi-Ou, Wang Yu-Gang, Li Fu-Quan, Zhang School of Computer Science and Technology Beijing Institute of Technology Beijing People’s Republic of China Beijing Institute of Science and Technology Information Beijing People’s Republic of China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou People’s Republic of China

A correction to this paper has been published: https://***/10.1007/s11227-021-03680-0

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Difference-aware knowledge selection for knowledge-grounded conversation generation

arXiv

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arXiv 2020年

作者： Zheng, Chujie Cao, Yunbo Jiang, Daxin 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 Smart Platform Product Department Tencent Beijing China STCA NLP Group Microsoft Beijing China

In a multi-turn knowledge-grounded dialog, the difference between the knowledge selected at different turns usually provides potential clues to knowledge selection, which has been largely neglected in previous research. In this paper, we propose a difference-aware knowledge selection method. It first computes the difference between the candidate knowledge sentences provided at the current turn and those chosen in the previous turns. Then, the differential information is fused with or disentangled from the contextual information to facilitate final knowledge selection. Automatic, human observational, and interactive evaluation shows that our method is able to select knowledge more accurately and generate more informative responses, significantly outperforming the state-of-the-art baselines. The codes are available at https://***/chujiezheng/DiffKS. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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Spaceborne Large-Squint Terrain-Matching Synthetic Aperture Radar: Concept and technology

Spaceborne Large-Squint Terrain-Matching Synthetic Aperture ...

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Asian and Pacific Conference on Synthetic Aperture Radar (APSAR)

作者： Yan Wang Zhe Li Zegang Ding Tao Zeng Radar Research Lab Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9781728129129

ISBN: (纸本)9781728129136

This paper discusses a new spaceborne large-squint terrain-matching synthetic aperture radar (LSTM-SAR) to effectively image the terrains that do not spread along satellite orbit. Different from the traditional spaceborne SAR, the swath of the LSTM-SAR spreads along the target terrain, instead of the satellite orbit, to perform terrain-matched imaging with both high resolution and short data acquisition period. Aiming at giving a sketch of how the LSTM-SAR works, the paper focuses on discussing the following three main aspects: the data acquisition geometry, azimuth sampling manner and raw data focusing algorithm. Specifically, an optimum squint geometry is firstly suggested for two dimensional swath optimization; then two non-uniform azimuth sampling methods are discussed to avoid data loss induced by transmission blockage; lastly, a wide nonlinear chirp scaling based algorithm is discussed to focus the spatially variant spaceborne LSTM-SAR echo. The presented approach is validated via the computer simulations.

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Regulating Crystallinity Mismatch Between Donor and Acceptor to Improve Exciton/Charge Transport in Efficient Organic Solar Cells

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Angewandte Chemie 2024年第11期136卷

作者： Prof. Dr. Xunfan Liao Mingtao Liu Hongqiao Pei Dr. Peipei Zhu Xinxin Xia Zeng Chen Yihan Zhang Zhongyuan Wu Dr. Yongjie Cui Dr. Guodong Xu Mengyuan Gao Prof. Dr. Long Ye Dr. Ruijie Ma Prof. Dr. Tao Liu Prof. Dr. Xinhui Lu Prof. Dr. Haiming Zhu Prof. Dr. Yiwang Chen Key Laboratory of Fluorine and Silicon for Energy Materials and Chemistry of Ministry of Education College of Chemistry and Chemical Engineering Jiangxi Normal University 99 Ziyang Avenue Nanchang 330022 China Department of Physics Chinese University of Hong Kong New Territories Kowloon Hong Kong 999077 China State Key Laboratory of Modern Optical Instrumentation Key Laboratory of Excited State Materials of Zhejiang Province Department of Chemistry Zhejiang University Hangzhou Zhejiang 310027 China School of Materials Science & Engineering Tianjin Key Laboratory of Molecular Optoelectronic Sciences Tianjin University Tianjin 300350 China Department of Electronic and Information Engineering Research Institute for Smart Energy (RISE) The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong China Guangxi Key Lab of Processing for Nonferrous Metals and Featured Materials Key Lab of New Processing Technology for Nonferrous Metals and Materials Ministry of Education School of Resources Environments and Materials Guangxi University Nanning 530004 China

Achieving a more balanced charge transport by morphological control is crucial in reducing bimolecular and trap-assisted recombination and enhancing the critical parameters for efficient organic solar cells (OSCs). Hence, a facile strategy is proposed to reduce the crystallinity difference between donor and acceptor by incorporating a novel multifunctional liquid crystal small molecule (LCSM) BDTPF4-C6 into the binary blend. BDTPF4-C6 is the first LCSM based on a tetrafluorobenzene unit and features a low liquid crystal phase transition temperature and strong self-assembly ability, conducive to regulating the active layer morphology. When BDTPF4-C6 is introduced as a guest molecule into the PM6 : Y6 binary, it exhibits better compatibility with the donor PM6 and primarily resides within the PM6 phase because of the similarity-intermiscibility principle. Moreover, systematic studies revealed that BDTPF4-C6 could be used as a seeding agent for PM6 to enhance its crystallinity, thereby forming a more balanced and favourable charge transport with suppressed charge recombination. Intriguingly, dual Förster resonance energy transfer was observed between the guest molecule and the host donor and acceptor, resulting in an improved current density. This study demonstrates a facile approach to balance the charge mobilities and offers new insights into boosting the efficiency of single-junction OSCs beyond 20 %.

关键词： organic solar cells crystallinity difference liquid crystal small molecule energy transfer tetrafluorobenzene

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Integrating Dense LiDAR-Camera Road Detection Maps by a Multi-Modal CRF Model

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IEEE Transactions on Vehicular technology 2019年第12期68卷 11635-11645页

作者： Gu, Shuo Zhang, Yigong Tang, Jinhui Yang, Jian Alvarez, Jose M. Kong, Hui PCA Lab Key Lab of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security Sch. of Comp. Sci. and Engineering Nanjing Univ. of Sci. and Technol. Nanjing 210094 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China

Road detection is an important task in autonomous navigation systems. In this paper, we propose a road detection method via a LiDAR-camera fusion strategy to exploit both the range and color information. The whole system consists of three parts. In the LiDAR based part, we transform the discrete 3D LiDAR point clouds to continuous 2D LiDAR range images and propose a distance-aware height-difference based scanning approach to get the road estimations quickly. In the camera based part, we apply a light-weight transfer learning based road segmentation network. In the LiDAR-camera fusion part, we transform the detection results from LiDAR and camera to dense and binary ones to solve the data imbalance problem and fuse them in a multi-modal conditional random field (MM-CRF) framework. Experiments show that the proposed MM-CRF fusion method can operate in real-time and achieve competitive performance compared with the state-of-the-art road detection algorithms on the KITTI-Road benchmark. © 1967-2012 IEEE.

关键词： KITTI LiDAR-camera fusion road detection

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Learning data-adaptive non-parametric kernels

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2020年第1期21卷 8590-8628页

作者： Fanghui Liu Xiaolin Huang Chen Gong Jie Yang Li Li Department of Electrical Engineering ESAT-STADIUS KU Leuven Belgium Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China and Department of Computing Hong Kong Polytechnic University Hong Kong SAR China Department of Automation BNRist Tsinghua University China

In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.

关键词： support vector machines non-parametric kernel learning gradient-Lipschitz continuous

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Edge-guided non-local fully convolutional network for salient object detection

arXiv

引用

arXiv 2019年

作者： Tu, Zhengzheng Ma, Yan Li, Chenglong Tang, Jin Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China Institute of Physical Science and Information Technology Anhui University Hefei230601 China

Fully Convolutional Neural Network (FCN) has been widely applied to salient object detection recently by virtue of high-level semantic feature extraction, but existing FCN-based methods still suffer from continuous striding and pooling operations leading to loss of spatial structure and blurred edges. To maintain the clear edge structure of salient objects, we propose a novel Edge-guided Non-local FCN (ENFNet) to perform edge-guided feature learning for accurate salient object detection. In a specific, we extract hierarchical global and local information in FCN to incorporate non-local features for effective feature representations. To preserve good boundaries of salient objects, we propose a guidance block to embed edge prior knowledge into hierarchical feature maps. The guidance block not only performs feature-wise manipulation but also spatial-wise transformation for effective edge embeddings. Our model is trained on the MSRA-B dataset and tested on five popular benchmark datasets. Comparing with the state-of-the-art methods, the proposed method achieves the best performance on all datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Object detection

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