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IEEE Transactions on intelligent Vehicles 2024年

作者： Li, Gang Lu, Yi He, Bin Pang, Tao Cheng, Bin Zhang, Pengpeng College of Electronics and Information Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai200120 China Shanghai Sunshine Rehabilitation Center Shanghai201613 China National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai200120 China East China Institute of Computing Technology Shanghai201822 China Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai China College of Electronic and Information Engineering Tongji University Shanghai China

Completing the docking between two multi-rotor unmanned aerial vehicles (UAVs) in the air has great significance for expanding the application scenarios of multiple UAVs and improving their endurance capability. In this regard, this paper first designs a safe and autonomous docking scheme and constructs a sub-mother UAV *** address the issue of unstable flight in downwash airflow, this paper designs a high-performance control method suitable for the sub-mother UAV system based on Incremental Nonlinear Dynamic Inversion (INDI) and achieves robust attitude control with strong disturbance rejection capability. A UWB-based Ortho-UWB 3D positioning method is proposed in this paper with orthogonal installation, which, after being integrated with visual methods, enables high-precision 3D relative positioning during the docking process of the sub-mother UAV ***, the paper designs a complete docking process for the sub-mother UAVs and incorporates safety constraints into the docking process to ensure the safety of the docking process for both UAVs. Experiments of successfully docking the sub UAV to the mother UAV from bottom to top prove the feasibility and effectiveness of this scheme. © 2024 IEEE.

关键词： Disturbance rejection

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The Parables of the Mustard Seed and the Yeast: Extremely Low-Budget, High-Performance Nighttime Semantic Segmentation 39

The Parables of the Mustard Seed and the Yeast: Extremely Lo...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Shiqin Xu, Xin Chen, Haoyang Jiang, Kui Wang, Zheng School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)157735897X

Nighttime Semantic Segmentation (NSS) is essential to many cutting-edge vision applications. However, existing technologies overly rely on massive labeled data, whose annotation is time-consuming and laborious. In this paper, we pioneer a new task focusing on exploring the potential of training strategy and framework design with limited annotation to achieve high-performance NSS. Insufficient information at very low labeling budgets can easily lead to under-optimization or overfitting of the model. Our solution comprises two main components: i) a novel region-based active sampling strategy called Contextual-Aware Region Query (CARQ), which identifies highly informative target nighttime regions for labeling;and ii) an innovative Fragmentation Synergy Active Domain Adaptation framework (FS-ADA), which progressively broadcasts the limited annotation to the unlabeled regions, achieving high performance with a minimal annotation budget. Extensive experiments demonstrate that our method outperforms state-of-the-art UDA-NSS & ADA-SS methods across four day-to-nighttime benchmarks, and generalizes well to foggy, rainy, & snowy scenes. In particular only with 1% target nighttime data annotation, our method is on par with the mainstream fully-supervised methods on the BDD100K-Night val dataset. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Budget control

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Multi-Layer Visual Perception for No-Reference Image Quality Assessment

Multi-Layer Visual Perception for No-Reference Image Quality...

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IEEE International Conference on Electronic information and Communication technology (ICEICT)

作者： Junwei Qi Yingzhen Wang Qingchun Wang Yingsong Li College of Computer Science and Technology Harbin Engineering University Harbin China College of Information and Communication Engineering Harbin Engineering University Harbin China Shanghai Yanding Information Technology Co. Ltd. Shanghai China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei Anhui China

In this work, we propose a no-reference image quality evaluation approach, aiming to solve the problem that the traditional convolutional neural network is insufficient to express the global information of the image. Therefore, in order to make the captured image features have the coherence of global context information, we combine a Transformer structure widely used in NLP (natural language processing) with the traditional CNN to realize the attention mechanism in the human visual perception characteristics. Firstly, the self-attention mechanism of Transformer is used to learn the global representation of the image from the multi-layer features extracted from different levels of CNN. Additionally, to forecast the ultimate image quality score, the global and local attributes are combined. Finally, the proposed algorithm is evaluated on three public datasets commonly used in CSIQ [1], TID2013 [2] and LIVE [3]. Compared with eight traditional no-reference image quality evaluation algorithms and five methods based on deep learning, the proposed algorithm achieves competitive results.

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Fusion of Optical and SAR Images Via NDVI-Like Images to Reconstruct NDVI Time Series in Cloud-Prone Regions

Fusion of Optical and SAR Images Via NDVI-Like Images to Rec...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yuyun Liang Yong Deng Jun Li Yunfei Li Fan Li Shaoquan Zhang School of Computer Science China University of Geosciences Wuhan China Changjiang Institute of Survey Planning Design and Research Wuhan China School of Geography and Planning Sun Yat-sen University Guangzhou China Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing School of Information Engineering Nanchang Institute of Technology Nanchang China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Normalized Difference Vegetation Index (NDVI) data from optical satellites have been widely used in the field of remote sensing. However, due to cloud cover and other extreme weather reasons, NDVI time series are often missing. In order to fill the observation gap under severe weather conditions, an NDVI time series reconstruction method that fuses optical and synthetic aperture radar (SAR) images has been developed. However, optical images often suffer from long-term data missing, severely limiting the usability of this reconstruction method. To solve this problem, this paper proposes a NDVI-like image strategy to avoid reliance on optical data. Specifically, the strategy obtains NDVI-like images by fitting the multivariate linear relationship between VV, VH polarization bands of SAR images and NDVI images. Then, the optical image information is represented by NDVI-like images, and the SAR and NDVI-like time series are input into the time series reconstruction network to reconstruct the NDVI time series in cloudy areas. Experimentation has revealed that the NDVI-like image strategy can effectively supplements optical image information for the NDVI reconstruction network, reduce the the influence of SAR image speckle noise on reconstruction outcomes, and obtain better reconstruction effects.

关键词： Optical polarization Normalized difference vegetation index Clouds Time series analysis Optical imaging Adaptive optics Radar polarimetry

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Spatiotemporal Activity Modeling via Hierarchical Cross-Modal Embedding : Extended Abstract

Spatiotemporal Activity Modeling via Hierarchical Cross-Moda...

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International Conference on Data Engineering

作者： Yang Liu Xiang Ao Linfeng Dong Chao Zhang Jin Wang Qing He Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Zhejiang University Hangzhou China College of Computing Georgia Tech United States Megagon Labs Mountain View United States

With the ever-increasing urbanization process, modeling people’s spatiotemporal activities from their online traces has become a crucial task. State-of-the-art methods for this task rely on cross-modal embedding, which maps items from different modalities (e.g., location, time, text) into the same latent space. Despite their inspiring results, existing cross-modal embedding methods merely capture co-occurrences between items without modeling their high-order interactions. In this paper, we first construct the user interaction graph and the activity graph from raw data records and propose a hierarchical cross-modal embedding method that takes the high-order relationships into consideration. We introduce both inter-record and intra-record meta-graph structures, which enable learning distributed representations that preserve high-order proximities across graphs from different layers. Our empirical experiments on three real-world datasets demonstrate that our method not only outperforms state-of-the-art methods for spatiotemporal activity prediction but also captures cross-modal proximity at a finer granularity.

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CCA-Net: Zero-shot SSVEP classification via an integration of canonical correlation analysis and deep neural network

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Deng, Yang Ji, Zhiwei Wang, Yijun Zhou, S. Kevin Harbin Institute of Technology School of Life Science and Technology Heilongjiang Harbin150001 China University of Science and Technology of China Division of Life Sciences and Medicine School of Biomedical Engineering Anhui Hefei230026 China Suzhou Institute for Advanced Research Jiangsu Suzhou215123 China Nanjing Agricultural University College of Artificial Intelligence Jiangsu Nanjing210095 China Chinese Academy of Sciences State Key Laboratory on Integrated Optoelectronics Institute of Semiconductors Beijing100083 China University of Science and Technology of China Key Laboratory of Precision and Intelligent Chemistry Anhui Hefei230026 China Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology Beijing100190 China

Accurate decoding of electroencephalographic (EEG) signals is a crucial foundation for brain-computer interface (BCI) applications. Among various decoding approaches, those that do not require calibration are particularly significant for advancing BCI technologies into everyday life, as they alleviate user fatigue by eliminating the need for user-specific training. In this study, we integrate a deep neural network with canonical correlation analysis (CCA) to form the CCA-Net approach for decoding steady-state visually evoked potential (SSVEP) based BCI without user-specific calibration, i.e., at the zero-shot scenario. The CCA-Net aims to reduce the cross-subject domain gap for better transfer learning and make full use of the testing signal itself. Specifically, it adapts the cross-subject dual-domain fusion network (CSDuDoFN) by transferring the global multi-reference least-squares transformation coefficient from source subjects to both source and target data to reduce the cross-subject domain gap. In addition, CCA-Net enhances the transfer template-based canonical correlation analysis (tt-CCA) by introducing a subject-specific template to replace the sine-cosine reference template and adding an extra CCA coefficient to make full use of the testing signal itself, resulting in the Modified tt-CCA. Finally, the features extracted by CSDuDoFN and Modified tt-CCA are integrated to produce the final decoding result. Our approach leverages the strengths of both data-driven and model-driven methods and achieves state-of-the-art performance on three publicly available datasets, thus holding the potential to facilitate everyday BCI applications based on SSVEP. The reproducibility code is available at: https://***/Sungden/Zero-shot-SSVEP-classification. © 1963-2012 IEEE.

关键词： Deep neural networks

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Modality-Aware Contrastive Instance Learning with Self-Distillation forWeakly-Supervised Audio-Visual Violence Detection

arXiv

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arXiv 2022年

作者： Yu, Jiashuo Liu, Jinyu Cheng, Ying Feng, Rui Zhang, Yuejie School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University China Academy for Engineering and Technology Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University China

Weakly-supervised audio-visual violence detection aims to distinguish snippets containing multimodal violence events with video-level labels. Many prior works perform audio-visual integration and interaction in an early or intermediate manner, yet overlooking the modality heterogeneousness over the weakly-supervised setting. In this paper, we analyze the modality asynchrony and undifferentiated instances phenomena of the multiple instance learning (MIL) procedure, and further investigate its negative impact on weakly-supervised audio-visual learning. To address these issues, we propose a modality-aware contrastive instance learning with self-distillation (MACIL-SD) strategy. Specifically, we leverage a lightweight two-stream network to generate audio and visual bags, in which unimodal background, violent, and normal instances are clustered into semi-bags in an unsupervised way. Then audio and visual violent semi-bag representations are assembled as positive pairs, and violent semi-bags are combined with background and normal instances in the opposite modality as contrastive negative pairs. Furthermore, a self-distillation module is applied to transfer unimodal visual knowledge to the audio-visual model, which alleviates noises and closes the semantic gap between unimodal and multimodal features. Experiments show that our framework outperforms previous methods with lower complexity on the large-scale XD-Violence dataset. Results also demonstrate that our proposed approach can be used as plug-in modules to enhance other networks. Codes are available at https://***/JustinYuu/MACIL-SD. Copyright © 2022, The Authors. All rights reserved.

关键词： Distillation

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Fundus Image Quality Assessment and Enhancement: a Systematic Review

arXiv

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arXiv 2025年

作者： Li, Heng Li, Haojin Ou, Mingyang Yu, Xiangyang Zhang, Xiaoqing Niu, Ke Fu, Huazhu Liu, Jiang Research Institute of Trustworthy Autonomous Systems SUSTech Shenzhen China Department of Computer Science and Engineering SUSTech Shenzhen China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Computer School Beijing Information Science and Technology University Beijing China Singapore

As an affordable and convenient eye scan, fundus photography holds the potential for preventing vision impairment, especially in resource-limited regions. However, fundus image degradation is common under intricate imaging environments, impacting following diagnosis and treatment. Consequently, image quality assessment (IQA) and enhancement (IQE) are essential for ensuring the clinical value and reliability of fundus images. While existing reviews offer some overview of this field, a comprehensive analysis of the interplay between IQA and IQE, along with their clinical deployment challenges, is lacking. This paper addresses this gap by providing a thorough review of fundus IQA and IQE algorithms, research advancements, and practical applications. We outline the fundamentals of the fundus photography imaging system and the associated interferences, and then systematically summarize the paradigms in fundus IQA and IQE. Furthermore, we discuss the practical challenges and solutions in deploying IQA and IQE, as well as offer insights into potential future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Photography

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DOA Estimation for RIS Assisted Multi-User Wireless Communication System with Squared-Sine Error Criterion and Lawson Norm Constraint

DOA Estimation for RIS Assisted Multi-User Wireless Communic...

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IEEE International Conference on Electronic information and Communication technology (ICEICT)

作者： Canping Yu Li Li Yingsong Li Dazhi Sun Liping Li Zhixiang Huang Tian Hong Loh Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Aircraft Airworthiness Institute China Academy of Civil Aviation Science and Technology (Center of Aviation Safety Technology CAST) Beijing China Electromagnetic and Eletrochemical Technologies Department National Physical Laboratory Teddington Middlesex UK

Reconfigurable intelligent surface (RIS) can control the phase of reflected signals, and has become a hotspot in the field of wireless communication. In this paper, we propose a direction-of-arrival (DOA) estimation algorithm for RIS assisted multi-user wireless communication systems with squared-sine error criterion and Lawson norm constraint. This algorithm utilizes the sparsity of the signal and can achieve fast and accurate DOA estimation under the interference of impulsive noises. To suppress the line-of-sight (LOS) interference and thus to maximize the received signal-to-interference-plus-noise ratio (SINR), we also propose a random optimal phase selection (ROPS) technique to design the control matrix of RIS elements. The simulation results show that the proposed algorithm performs better than the existing methods.

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Learning Adaptability Analysis for Remote Sensing Image Matching via Multi-Scale Saliency and Uniqueness Feature Fusion

Learning Adaptability Analysis for Remote Sensing Image Matc...

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Advanced Algorithms and Control Engineering (ICAACE), International Conference on

作者： Shichao Chen Xu Zou Kaifeng Hu Mengzhen Qi Jing Li Sheng Zhong Kaibo Zhou National Key Laboratory of Intelligent Processing Technology for Multispectral Information School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan People's Republic of China Belt and Road Joint Laboratory on Measurement and Control Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan People's Republic of China Key Laboratory of Smart Earth Beijing People's Republic of China Beijing Institute of Tracking and Telecommunications Technology Beijing People's Republic of China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

Image matching navigation technology, known for its exceptional precision and anti-interference capabilities, has been widely applied in aircraft navigation. Adaptability analysis, a core component of this technology, is critical for enhancing matching performance. Existing deep learning-based adaptability analysis methods lack feature extraction networks specifically designed for adaptability analysis, which hinders their ability to fully capture the saliency and uniqueness features within adaptability regions. This limitation consequently restricts their performance in the adaptability analysis of remote sensing images. A novel remote sensing image adaptability network architecture named US-SwinT is proposed. The network consists of two core modules: saliency analysis and uniqueness analysis, designed to effectively utilize regional scene perception to accurately predict matchability of reference images. The multi-scale saliency analysis module adaptively fuses global contextual information with local detail features through a dynamic weighting mechanism, enhancing the model's ability to discern key regions. The uniqueness module employs cosine similarity to analyze feature similarity between regions, effectively assessing redundancy of regional features. By suppressing highly similar regional features, it enhances capture of regional differences. Experimental results demonstrate that US-SwinT outperforms traditional methods and other deep learning models across multiple key metrics, significantly improving accuracy and robustness of remote sensing image adaptability analysis.

关键词： Analytical models Adaptation models Accuracy Image matching Feature extraction Transformers Aircraft navigation Stability analysis Robustness Remote sensing

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