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Spatial-Temporal Semantic Feature Interaction Network for Semantic Change Detection in Remote Sensing Images

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12090-12102页

作者： Zhang, Yuhang Zhang, Wuxia Ding, Songtao Wu, Siyuan Lu, Xiaoqiang Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing School of Computer Science and Technology Xi'an710121 China Xi'an University of Technology College of Computer Science and Engineering Shaanxi Xi'an710048 China Fuzhou University College of Physics and Information Engineering Fuzhou350002 China

Semantic Change Detection (SCD) in Remote Sensing Images (RSI) aims to identify changes in the type of Land Cover/Land Use (LCLU) corresponding to changed areas in RSI. The "from-to" information of the acquired image has more profound practical significance than Binary Change Detection (BCD). However, most deep learning-based SCD algorithms do not fully exploit the spatial-temporal information of multi-level features, leading to challenges in extracting LCLU features in complex changing scenes. To address these issues, we propose a Spatial-Temporal Semantic Feature Interaction Network (STS-FINet) to improve the performance of SCD in RSI. The proposed STS-FINet comprises a Multi-Scale Feature Extraction Encoder (MS-FEE), a Transformer-based Multi-Level Feature Interaction module (TML-FI), and a Multi-Level Feature Fusion Decoder (ML-FFD). The MS-FEE extracts deep semantic and differential information from the RSI. The TML-FI is designed to mine the spatial-temporal information by extracting long-range dependencies and spatial information from multi-level features to improve spatial perception. Moreover, Mixed Spatial Reasoning Convolution block (MixSrc) is presented to enrich the spatial information by extracting the multi-scale features, thus improving the model's capability to interpret complex changing scenes. Finally, ML-FFD integrates the multi-level features, resulting in the generation of the semantic change map. The effectiveness of the proposed STS-FINet is verified on two high-resolution RSI datasets. Experimental results show that the proposed STS-FINet achieves better change detection performance than SOTA methods. © 2008-2012 IEEE.

关键词： Change detection

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Hopf and double Hopf bifurcations in a delayed lateral vibration model of footbridges induced by pedestrians

arXiv

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arXiv 2025年

作者： Li, Xuemei Liu, Yechi Key Laboratory of High Performance Computing and Stochastic Information Processing Department of Mathematics Hunan Normal University Hunan Changsha410081 China

In this paper, we investigate the dynamical behaviors of a delayed lateral vibration model of footbridges proposed based on the facts that pedestrians will reduce their walking speed or stop walking when the response of the footbridge becomes sufficiently large, and that the bridge velocity can not be changed at once when the pedestrians begin to walk on the bridge. By analyzing the distribution of roots of the associated characteristic equation, we find that there are only two types of bifurcations in this model: Hopf bifurcation and double Hopf bifurcation, and give the condition on the stability of the trivial solution. By using the center manifold theorem and bifurcation theory of delayed differential equations, we obtain the dynamical behavior in these bifurcations, specially including the stability of periodic solutions and invariant tori bifurcating from the trivial solution in these bifurcations. Finally, we prove that this model exhibits quasi-periodic vibrations by KAM theorems, besides periodic vibrations. © 2025, CC BY-NC-ND.

关键词： Hopf bifurcation

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Optically transparent transmissive orbital angular momentum (OAM) metasurface with high transmittance and broadband characteristics

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Optics Express 2025年第12期33卷 24385-24395页

作者： Yong Cai Yuying Dai Yue Ma Gang Wang Guanghui Xu Xingang Ren Hui Wang Xiaohu Zhao Xianliang Wu Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei 230039 China Key Laboratory of Electromagnetic Environmental Sensing of Anhui Higher Education Institutes Anhui University Hefei 230039 China wang_toward@*** ghxu86@*** Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Institute of Physical Science and Information Technology Anhui University Hefei 230039 China xgren@*** Hefei Normal University Hefei 230009 China

This work proposes a high-performance broadband orbital angular momentum (OAM) beam-generating metasurface based on optically transparent media. A broadband polarization-converting transmissive metasurface, which operates in the 18 - 24 (28.57% fractional bandwidth) GHz range, has been realized by collaboratively designing dual C-split ring resonator structures and bidirectional lattice-patterned surface networks. The array structure demonstrates typical helical phase distribution features in the K-band, and experimental results verify simulation design. Innovatively employing a composite structure of quartz glass substrate and patterned conductive thin films, the design achieves compatible optimization of 77% visible-light transmittance and microwave broadband performance. This approach breaks through the mutually exclusive limitations of traditional metasurfaces’ optical/electromagnetic properties, providing a highly integrated solution for transparent antenna radomes in satellite communication systems and multi-mode beam multiplexing applications.

关键词： Orbital angular momentum multiplexing Phase plates Ring resonators Spatial light modulators Spiral phase Thin films

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High-precision end-to-end adaptive optics aided orbital angular momentum wireless communication with XFTC-Net

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Optics Letters 2025年第10期50卷 3265-3268页

作者： Panpan Xu Huan Chang Fei Wang Ran Gao Dong Guo Lei Zhu Zhipei Li Sitong Zhou Qi Xu Jing Chen Xiangjun Xin School of Information and Electronics Beijing Institute of Technology (BIT) Beijing 100081 China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China School of Electronic Engineering Beijing University of Posts and Telecommunications (BUPT) Beijing 100876 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology 250353 China

In this Letter, we demonstrate high-precision end-to-end adaptive optics (AO) technique based on the X-Shape Fusion Transformer-convolutional neural network (XFTC-Net) without an additional probe path to compensate for distortions caused by atmospheric turbulence (AT) in orbital angular momentum (OAM) optical wireless communication links. The experimental results show that the XFTC-Net effectively extracts the intensity distribution features of distorted OAM beams, predicting AT phase screens and identifying OAM modes. After XFTC-Net-aided AO compensation, the bit error rate of the OAM optical communication system remains below 3.8 × 10 −3 , staying within $C_{n}^2=1 \times 10^{-15}$ –1 × 10 −13 m −2/3 . Moreover, the optical power with l = 3 increases from 5.08 dBm to 8.23 dBm at a transmission distance of 800 m. These experimental results indicate that the proposed XFTC-Net AO technique effectively compensates for OAM beam distortion in both single- and multiplexed-channel OAM links, which effectively balances the system complexity and transmission performance of OAM-based free space optical (FSO) communication systems, opening up new avenues for enhancing the performances of high-capacity optical communication.

关键词： Adaptive optics Bit error rate Neural networks Optical wireless communication Polarization division multiplexing Space division multiplexing

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Textured Surface Defect Detection with Image-Level Accuracy Optimization via Patch-Level Features and Simulation-Based Samples

Textured Surface Defect Detection with Image-Level Accuracy ...

引用

Algorithms, computing and Machine Learning (CACML), Asia Conference on

作者： Fen Li Dongxiao Li Le Huang Hangrui Chen Ming Zhang Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing College of Information Science and Electronic Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798331543143

ISBN: (纸本)9798331543150

Textured surface defect detection plays a critical role in industrial product quality monitoring. However, excessive reliance on pixel-level precision, especially when defect boundaries are unclear, can lead to more complex models, lower efficiency, and increased risk of false positives or missed detections, ultimately compromising system reliability. Additionally, challenges such as the difficulty of collecting defect samples, severe class imbalances, and high pixel-level labeling costs hinder the effectiveness of convolutional neural networks in terms of both inference speed and computational efficiency. To overcome these challenges, we propose SiamSimNet, a dual-channel Siamese network that relies on high-quality training data generated through simulation algorithms, minimizing the need for real defect samples. By incorporating locally aware patch features, the proposed method significantly improves detection accuracy and robustness, while maintaining high inference efficiency. This approach shows strong potential for real-world industrial applications.

关键词： Accuracy Costs Computational modeling Feature extraction Robustness Surface texture Computational efficiency Labeling Monitoring Defect detection

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Object-Aware Video Matting with Cross-Frame Guidance

arXiv

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arXiv 2025年

作者： Zhang, Huayu Wu, Dongyue Shao, Yuanjie Sang, Nong Gao, Changxin National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology China

Recently, trimap-free methods have drawn increasing attention in human video matting due to their promising performance. Nevertheless, these methods still suffer from the lack of deterministic foreground-background cues, which impairs their ability to consistently identify and locate foreground targets over time and mine fine-grained details. In this paper, we present a trimap-free Object-Aware Video Matting (OAVM) framework, which can perceive different objects, enabling joint recognition of foreground objects and refinement of edge details. Specifically, we propose an Object-Guided Correction and Refinement (OGCR) module, which employs cross-frame guidance to aggregate object-level instance information into pixel-level detail features, thereby promoting their synergy. Furthermore, we design a Sequential Foreground Merging augmentation strategy to diversify sequential scenarios and enhance capacity of the network for object discrimination. Extensive experiments on recent widely used synthetic and real-world benchmarks demonstrate the state-of-the-art performance of our OAVM with only an initial coarse mask. The code and model will be available. © 2025, CC BY.

关键词： Object recognition

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Anchored Monotonic Alignment and Representation Substitution for Rare Spontaneous Behaviors in Spontaneous Speech Synthesis

Anchored Monotonic Alignment and Representation Substitution...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Wu, Ning-Qian Hu, Ya-Jun Chen, Liping Ling, Zhen-Hua National Engineering Research Center of Speech and Language Information Processing University of Science and Technology of China Hefei China iFLYTEK Research iFLYTEK Co. Ltd. China MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition University of Science and Technology of China China

ISBN: (纸本)9798350368741

Spontaneous behaviors in speech pose significant challenges for speech synthesis. Existing research has not adequately addressed these behaviors, with most studies relying on specially recorded datasets. In contrast, real-world data more accurately reflects the natural, spontaneous speaking styles in everyday life and encompasses a wider range of spontaneous behaviors. However, such data is often of lower quality, and the distribution of spontaneous behaviors is highly imbalanced. In this study, we explore spontaneous speech synthesis using real-world data within the VITS2 framework. To overcome these challenges, we introduce two techniques: anchored monotonic alignment and spontaneous hidden representation substitution. Experimental results demonstrate that these methods enhance model alignment and improve the naturalness of the generated speech. Our proposed approach successfully addresses the challenge of synthesizing rare spontaneous behaviors and offers users flexible control over the synthesized speech. © 2025 IEEE.

关键词： real-world data speech synthesis spontaneous behaviors spontaneous speech synthesis VITS

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React Agent-Based Question Answering Method for Biology in National College Entrance Examination 24

React Agent-Based Question Answering Method for Biology in N...

引用

5th International Symposium on Artificial Intelligence for Medicine Science, ISAIMS 2024

作者： Chen, Yulin Luo, Jing Tu, Xinhui College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei Wuhan China College of Computer Science Central China Normal University Hubei Wuhan China

ISBN: (纸本)9798400717826

Recent advancements in large language models have led to significant improvements in various natural language processing tasks, including automated question answering. However, these models still struggle with providing accurate responses to complex biology questions, such as those found in the National College Entrance Examination. To address this issue, researchers have explored retrieval-augmented generation techniques, which incorporate external knowledge sources to enhance answer accuracy. While these methods have shown promise in improving efficiency, they often lack the ability to perform in-depth analysis of complex questions. In response to these limitations, the paper proposes a novel ReAct agent-based question answering method. This method combines biology textbook document libraries with Google search engine capabilities, leveraging the strengths of large language models. By integrating these components, this method can engage in autonomous decision-making and multi-step reasoning, allowing for a more thorough analysis of complex biology questions. Experimental results demonstrate the effectiveness of this new approach. When compared to responses generated directly by ChatGPT and GPT-4o models, the method showed significant improvements in accuracy for biology questions from the National College Entrance Examination. Specifically, the new method achieved accuracy increases of 11.1% and 4.64% over ChatGPT and GPT-4o, respectively. © 2024 Copyright held by the owner/author(s).

关键词： Question answering

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DD-Net: Dynamic Network Architecture for Optimized Curve Segmentation and Reduce Computational Redundancy 27th

DD-Net: Dynamic Network Architecture for Optimized Curve Seg...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Cao, Yunxiang Chen, Li Wang, Yubo Feng, Zhida Tian, Jing School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China Institute of Systems Science National University of Singapore Singapore Singapore

ISBN: (纸本)9783031783821

Curvilinear Structure segmentation has numerous applications in various fields, including providing a better understanding of defects such as cracks on roads or walls, thereby assessing the structural safety of buildings. Numerous curve segmentation methods based on Convolutional Neural Networks(CNN) have been developed in recent years. However, preserving feature integrity remains a challenge. To address this issue, this paper introduces a double dynamic network, called DD-Net, which consists of a dynamic structure for training and dynamic masking for inference. Firstly, to enhance the nonlinearity of the model, a paradigm to auto-adjust the CNN structure via a trainable module is formed for better fine-grained feature extraction. This dynamic structure (DS) paradigm enables a trade-off to extract or discard the feature data in the model training. Due to the data heterogeneity between the training and inference data, models may contain some useless nodes that are less effective during inference. The dynamic masking (DM) is proposed to omit the useless nodes based on the score difference between the train and inference feature statistics, thereby reducing redundant computations. To further improve the model’s performance on thin-curve segmentation and to preserve feature integrity, we introduce a non-curve suppression (NCS) module. This module focuses on background information while considering foreground prediction to address noisy conditions. The experimental results show that our DD-Net achieves promising results on three benchmark datasets and outperforms state-of-the-art curve segmentation models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Deep Supervised Cone-Based Nonnegative Matrix Factorization in Image Pattern Space 2nd

Deep Supervised Cone-Based Nonnegative Matrix Factorization ...

引用

2nd International Conference on Applied Intelligence, ICAI 2024

作者： He, Jinghui Chen, Wen-Sheng Pan, Binbin Chen, Bo School of Mathematical Sciences Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

ISBN: (纸本)9789819619030

Deep nonnegative matrix factorization (DNMF) is a promising approach for analyzing image data at hierarchical levels, as it can reveal hierarchical nonnegative features of the data in the image pattern space. Nevertheless, most existing DNMF models are unsupervised learning methods, making the image data fall in the same cone spanned by the basis images. This limitation potentially degrades the performance of the DNMF algorithms. To overcome the same cone problem, this paper proposes a novel deep-supervised cone-based NMF (DSCNMF) method. The DSCNMF model is established according to the fact that samples from the same class are confined within the same cone, whereas data from distinct classes occupy separate cones. The optimization objective of DSCNMF seeks to minimize the volume of each cone while maximizing the distance between different cones. Furthermore, graph regularization is integrated into the proposed model to preserve the local data structure. The multiplicative update formulas are acquired using the gradient descent method, and the DSCNMF algorithm is theoretically proven to be convergent. The experimental results conducted on two publicly available facial image datasets demonstrate the effectiveness and superiority of the proposed DSCNMF method compared with several state-of-the-art DNMF algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Face recognition

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