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检索条件"机构=Key Laboratory of Intelligent Information Perception and Processing Technology"

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SC-DVNet: A Dual-Branch Recognition Network for Synthetic Aperture Radar Images Based on Scattering Features

SC-DVNet: A Dual-Branch Recognition Network for Synthetic Ap...

引用

Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Fan Chen Hao Shi Jingfei He Yifei Yin Fanyu Meng Liang Chen National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Ship classification using Synthetic Aperture Radar (SAR) images is a crucial component in marine monitoring. With the rise of deep neural networks (DNN), the abstract feature maps obtained by CNN-based methods have significantly improved classification performance. However, most current DNN-based methods are predominantly inherited from optical imagery, designed only for SAR intensity images, which may present challenges for further improving recognition performance. Therefore, this paper proposes using the point Scattering Center (SC) model for scattering feature extraction combined with graph convolutional networks for ship classification tasks. Comprehensive ablation experiments were carried out to demonstrate the efficacy of the proposed approach. Finally, experimental results on OpenSARShip1.0 dataset verify that the performance of the suggested approach notably outperforms DNN-driven techniques.

关键词： Integrated optics Image recognition Scattering Artificial neural networks Feature extraction Optical imaging Adaptive optics Optical scattering Marine vehicles Synthetic aperture radar

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Tuning Geometric Conformations of Curved DNA Structures by Controlling Positions of Nicks 1

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17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Xie, Chun Hu, Yingxin Chen, Kuiting Chen, Zhekun Pan, Linqiang Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China College of Information Science and Technology Shijiazhuang Tiedao University Shijiazhuang050043 China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

DNA origami is one of the powerful techniques that utilize DNA as building blocks to synthesize nanostructures. Traditionally, through introducing different numbers of insertions and deletions of base pairs in DNA helices, the in-plane bending angle of curved DNA structures could be roughly tuned. Here, we explored a strategy that used the position patterns of nicks in staple strands to tune the geometric conformation of curved DNA origami structures, including in-plane bending, out-of-plane bending, and twisting angles. When the structure adopted different patterns of nicks positions, great difference appeared in the geometric properties. Further, by combining subunits of different nicks position patterns, the bending and twisting of the combined structure was effectively tuned. The strategy increases the design accuracy of curved DNA origami structures and expands the toolbox for designing DNA structures. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Molecular dynamics

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Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer 29

Improving Fake News Detection of Influential Domain via Doma...

引用

29th International Conference on Computational Linguistics, COLING 2022

作者： Nan, Qiong Wang, Danding Zhu, Yongchun Sheng, Qiang Shi, Yuhui Cao, Juan Li, Jintao Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

Both real and fake news in various domains, such as politics, health, and entertainment are spread via online social media every day, necessitating fake news detection for multiple domains. Among them, fake news in specific domains like politics and health has more serious potential negative impacts on the real world (e.g., the infodemic led by COVID-19 misinformation). Previous studies focus on multi-domain fake news detection, by equally mining and modeling the correlation between domains. However, these multi-domain methods suffer from a seesaw problem: the performance of some domains is often improved at the cost of hurting the performance of other domains, which could lead to an unsatisfying performance in specific domains. To address this issue, we propose a Domain- and Instance-level Transfer Framework for Fake News Detection (DITFEND), which could improve the performance of specific target domains. To transfer coarse-grained domain-level knowledge, we train a general model with data of all domains from the meta-learning perspective. To transfer fine-grained instance-level knowledge and adapt the general model to a target domain, we train a language model on the target domain to evaluate the transferability of each data instance in source domains and re-weigh each instance’s contribution. Offline experiments on two datasets demonstrate the effectiveness of DITFEND. Online experiments show that DITFEND brings additional improvements over the base models in a real-world scenario. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Fake detection

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DIR-YOLOv5: A real-time Drone-perspective Infrared object detection method based on YOLOv5

DIR-YOLOv5: A real-time Drone-perspective Infrared object de...

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2023 Applied Optics and Photonics China: Infrared Devices and Infrared technology;and Terahertz technology and Applications, AOPC 2023

作者： Yuexing, Wang Chuangang, Xu Jianzhong, Su Jinwen, Tian National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Tianjin Jinhang Institute of Technical Physics Tianjin300308 China

ISBN: (纸本)9781510672260

With the advancement of drone technology, object detection from the perspective of drones has found extensive applications in various fields, including surveillance, search operations, and reconnaissance tasks. Currently, most drones in the market are equipped with visible light imagers, while some high-end drones are equipped with infrared imaging detectors capable of performing infrared object detection tasks. Infrared imaging utilizes a passive imaging mode, enabling it to detect thermal radiation emitted by objects. As a result, it offers the distinct advantage of continuous operation without being restricted by daylight conditions. In comparison to visible imaging, infrared imaging uses longer wavelengths and possesses a certain level of penetration capability through clouds and smoke. Consequently, infrared object detection represents a significant research area within the field of object detection. However, detecting infrared objects, especially small ones, remains challenging due to the complexity of background information, lower resolution compared to visible images, and the lack of shape and texture information in infrared images. In response to these challenges, this study proposes a real-time drone-perspective infrared (IR) object detection method based on the YOLOv5 framework, known as DIR-YOLOv5. To effectively address the challenge of infrared vehicles occupying fewer pixels in the drone's perspective image and making objects difficult to detect, the coordinate attention (CA) for feature enhancement is introduced. we also introduce a Spatial-Channel dynamic and query-aware sparse attention mechanism (SCBiFormer), which is optimized based on BiFormer. Additionally, we redefine the loss function as the Repulsion Loss function to tackle the problem of infrared vehicle objects gathering and overlapping occlusion in scenarios like parking lots. Furthermore, we expand the ISVD infrared image object detection dataset to include multiple scenarios and conduct

关键词： Image enhancement

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ExpTopo: Explicit Topological Modeling for Bronchus Segmentation 22

ExpTopo: Explicit Topological Modeling for Bronchus Segmenta...

引用

22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Zhao, Mingyue Qiu, Xiaolan Zhou, S.Kevin School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China (USTC) China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advance Research Ustc China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou Aerospace Information Research Institute China Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China (USTC) China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology China

ISBN: (纸本)9798331520526

Airway extraction is paramount in the early diagnosis and treatment of respiratory diseases. As a tree-like structure, both topological-aware learning and voxel-wise classification are equally crucial for the airway. However, existing methods demonstrate insufficient topological learning, emphasizing only the supervision of individual key topological points. Consequently, this paper proposes a Explicit Topological Modeling (ExpTopo) approach to aid airway segmentation. It explicitly introduces topological metric space learning based on semantic segmentation, enhancing the model's structural perception by implementing global skeleton-level sparse topological learning (STL) and local voxel-level dense topological perception (DTP). Extensive experimental results demonstrate that the algorithm achieves competitive performance at both the topological and voxel levels. Code will be available in https://***/MorineZ/ExpTopo. © 2025 IEEE.

关键词： Airway extraction dense topological perception explicit topological modeling sparse topological learning

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Visual Question Answering of Remote Sensing Image Based on Attention Mechanism 5th

Visual Question Answering of Remote Sensing Image Based on...

引用

5th IFIP TC 12 International Conference on Intelligence Science, ICIS 2022

作者： Zhang, Shihuai Wei, Qiang Li, Yangyang Chen, Yanqiao Jiao, Licheng The Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education International Research Center for Intelligent Perception and Computation Joint International Research Laboratory of Intelligent Perception and Computation Collaborative Innovation Center of Quantum Information of Shaanxi Province School of Artificial Intelligence Xidian University Xi’an710071 China The Key Laboratory of Aerospace Information Applications The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang050081 China

ISBN: (纸本)9783031149023

In recent years, the research of attention mechanism has made significant progress in the field of computer vision. In the processing of visual problems of remote sensing images, the attention mechanism can make the computer focus on important image areas and improve the accuracy of question answering. Our research focuses on the role of synergistic attention mechanisms in the interaction of question representations and visual representations. On the basis of Modular Collaborative Attention (MCA), according to the complementary characteristics of global features and local features, the hybrid connection strategy is used to perceive global features at the same time without weakening the attention distribution of local features. The impact of attention mechanisms on various types of visual question answering questions has been evaluated:(i) scene classification (ii)object comparison (iii) quantitative statistics (iv) relational judgment. By fusing the global features and local features of different modalities, the model can obtain more information between modalities. Model performance evaluation under the RSVQA-LR dataset. Experimental results show, the method in this paper improves the global accuracy by 9.81% than RSVQA. © 2022, IFIP International Federation for information processing.

关键词： Image enhancement

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Research on financial social public opinion communication model based on variation mechanism 22

Research on financial social public opinion communication mo...

引用

Proceedings of the 2022 6th International Conference on Electronic information technology and Computer Engineering

作者： Maojun Huang Mei Hong Lin Dong Da Yuan Key Laboratory of Intelligent Information Processing Shandong Technology and Business University China

ISBN: (纸本)9781450397148

In the context of rapid development of the Internet, the place of spreading financial public opinion is converted from traditional offline places to major online social platforms. Mastering the development mechanism of financial social opinion dissemination on online social media can effectively estimate the length of influence of public opinion and the scope of affected people, and provide effective guidance to relevant staff. Based on the Susceptible Infected Recovered Model, this paper divides the people involved in opinion diffusion into commenters and discussers, and introduces the variation mechanism to design the Susceptible-comment-discussion-removal model, then simulates the model to study the effects of different initial states and parameters on the model, and finally verifies the validity of the model by combining the real data of stock bars. The simulation experiments and validation show that the model can effectively describe the spread of public opinion among the user groups of financial social platforms when it occurs, and provide a valid reference for related workers. However, the content of public opinion, the personal influence of communicators, and the lag effect of communication all have an impact on communication, and these issues need to be addressed in future research.

关键词： variation mechanism financial social opinion SIR model public opinion dissemination SCDR model

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Dynamic small area visual target tracking discrimination algorithm and animal welfare evaluation applications 3

Dynamic small area visual target tracking discrimination alg...

引用

3rd International Conference on information Science, Parallel and Distributed Systems, ISPDS 2022

作者： Wang, Yu Fang, Jiandong Zhao, Yudong Inner Mongolia University of Technology College of Information Engineering China Key Laboratory of Perception Technology and Intelligent Systems Inner Mongolia Autonomous Region Inner Mongolia Hohhot China

ISBN: (数字)9781665487474

ISBN: (纸本)9781665487474

Automatic monitoring and evaluation of cattle welfare status in smart pastures requires tracking and identification of the target cattle's ear area and morphology based on video images. Most of the traditional methods use contact detection, which is somewhat invasive and easy to cause cattle stress reaction. In this paper, we design a dynamic small area tracking discrimination algorithm based on the marker less posture estimation method, which includes the tracking matching model of key points, the relative fluctuation model of cattle ear and the fluctuation behavior evaluation model in turn, and finally realize the automatic recognition of motion cattle ear area and morphological features. Through simulation experiments, the effectiveness and feasibility of the method are demonstrated. © 2022 IEEE.

关键词： Target tracking

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Spatio-Temporal Context Modeling for Road Obstacle Detection 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Wu, Xiuen Wang, Tao Liang, Lingyu Li, Zuoyong Ching, Fum Yew College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China Ministry of Education Key Laboratory of Computer Network and Information Integration Southeast University Nanjing211189 China Guangzhou510320 China Penang11800 Malaysia

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

Road obstacle detection is an important problem for vehicle driving safety. In this paper, we aim to obtain robust road obstacle detection based on spatio-temporal context modeling. Firstly, a data-driven spatial context model of the driving scene is constructed with the layouts of the training data. Then, obstacles in the input image are detected via the state-of-the-art object detection algorithms, and the results are combined with the generated scene layout. In addition, to further improve the performance and robustness, temporal information in the image sequence is taken into consideration, and the optical flow is obtained in the vicinity of the detected objects to track the obstacles across neighboring frames. Qualitative and quantitative experiments were conducted on the Small Obstacle Detection (SOD) dataset and the Lost and Found dataset. The results indicate that our method with spatio-temporal context modeling is superior to existing methods for road obstacle detection. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Dynamic Mask Curriculum Learning for Non-Autoregressive Neural Machine Translation 1

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18th China Conference on Machine Translation, CCMT 2022

作者： Wang, Yisong Hou, Hongxu Sun, Shuo Wu, Nier Jian, Weichen Yang, Zongheng Wang, Pengcong National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Key Laboratory of Mongolian Information Processing Technology College of Computer Science Inner Mongolia University Hohhot China

ISBN: (数字)9789811979606

ISBN: (纸本)9789811979590

Non-autoregressive neural machine translation is gradually becoming a research hotspot due to its advantages of fast decoding. However, the increase of decoding speed is often accompanied by the loss of model performance. The main reason is that the target language information obtained at the decoder side is insufficient, and the mandatory parallel decoding leads to a large number of mistranslation and missing translation problems. In order to solve the problem of insufficient target language information, this paper proposes a dynamic mask curriculum learning approach to provide target side language information to the model. The target side self-attention layer is added in the pre-training phase to capture the target side information and adjust the amount of information input at any time by way of curriculum learning. The fine-tuning and inference phases disable the module in the same way as the normal NAT model. In this paper, we experiment on two translation datasets of WMT16, and the BLEU improvement reaches 4.4 without speed reduction. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Curricula

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