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Bi-Temporal Feature Relational Distillation for On-Board Lightweight Change Detection in Remote Sensing Imagery

IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Wang, Guoqing Chen, He Li, Jie Wang, Jue Liu, Wenchao Chen, Liang Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing100081 China Shanghai Aerospace Electronic Technology Institute Shanghai201109 China

Deep learning models for remote sensing change detection (CD) require significant resources, challenging real-time applications on spaceborne devices. Knowledge distillation (KD) technology is a potential solution that balances model size and CD performance. However, existing KD methods struggle to effectively transfer the teacher's ability to respond to changed landcovers, and also face challenges in assisting the student model in detecting changes in landcovers with subtle visual characteristics. To overcome these limitations, a bi-temporal feature relational distillation (FRD) framework is proposed. The FRD framework includes two designed distillation components: bi-temporal feature distance distillation (BFDD) and contrastive cluster representation distillation (CCRD). BFDD guides the student model to learn the relationship between bi-temporal feature categories from the teacher model and introduces the iteration-wise random selection and bilinear interpolation strategy to solve feature mismatch problem between the teacher and the student. CCRD describes the pixel-level and clusterlevel feature distributions through the designed contrastive cluster (CC) module, guiding the student model to align the change/nonchange features with the teacher model, so as to alleviate the confusion of change/non-change features. Extensive experiments and analyses have confirmed that the proposed FRD framework can develop lightweight models capable of achieving performance comparable to large models, thereby enhancing suitability for realtime CD. © 1965-2011 IEEE.

关键词： Change detection

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Attention-based UAV Trajectory Optimization for Wireless Power Transfer-assisted IoT Systems

arXiv

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arXiv 2025年

作者： Dong, Li Jiang, Feibo Peng, Yubo School of Computer Science Hunan University of Technology and Business Changsha410205 China Xiangjiang Laboratory Changsha410205 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China School of Intelligent Software and Engineering Nanjing University Suzhou215163 China

Unmanned Aerial Vehicles (UAVs) in Wireless Power Transfer (WPT)-assisted Internet of Things (IoT) systems face the following challenges: limited resources and suboptimal trajectory planning. Reinforcement learning-based trajectory planning schemes face issues of low search efficiency and learning instability when optimizing large-scale systems. To address these issues, we present an Attention-based UAV Trajectory Optimization (AUTO) framework based on the graph transformer, which consists of an Attention Trajectory Optimization Model (ATOM) and a Trajectory lEarNing Method based on Actor-critic (TENMA). In ATOM, a graph encoder is used to calculate the self-attention characteristics of all IoTDs, and a trajectory decoder is developed to optimize the number and trajectories of UAVs. TENMA then trains the ATOM using an improved Actor-Critic method, in which the real reward of the system is applied as the baseline to reduce variances in the critic network. This method is suitable for high-quality and large-scale multi-UAV trajectory planning. Finally, we develop numerous experiments, including a hardware experiment in the field case, to verify the feasibility and efficiency of the AUTO framework. Copyright © 2025, The Authors. All rights reserved.

关键词： Antennas

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VIPNet: Combining Viewpoint information and Shape Priors for Instant Multi-view 3D Reconstruction 17th

VIPNet: Combining Viewpoint Information and Shape Priors fo...

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17th Asian Conference on Computer Vision, ACCV 2024

作者： Ye, Weining Li, Zhixuan Jiang, Tingting School of Computer Science Peking University Beijing100871 China College of Computing and Data Science Nanyang Technological University Singapore Singapore National Engineering Research Center of Visual Technology National Key Laboratory for Multimedia Information Processing School of Computer Science National Biomedical Imaging Center Peking University Beijing100871 China

ISBN: (纸本)9789819609680

While the multi-view 3D reconstruction task has made significant progress, existing methods simply fuse multi-view image features without effectively leveraging available auxiliary information, especially the viewpoint information for guiding and associating features of different views. To this end, we propose to enhance multi-view 3D reconstruction with the power of viewpoint information. Specifically, a simple-yet-effective viewpoint estimator is designed to learn and provide comprehensive viewpoint knowledge for locating and associating learned features from different views. Moreover, to improve the 3D reconstruction quality when 2D images of only very few viewpoints are available, we propose to learn the shape prior knowledge to provide sufficient shape information for compensating the limited 2D observations. Overall, we present VIPNet, benefiting from Viewpoint information and Shape Prior learning for high-quality multi-view 3D reconstruction. Extensive experiments validate the effectiveness of the proposed VIPNet, which achieves state-of-the-art performance on challenging datasets and shows well generalization ability in real-world scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 3D modeling

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Modality Unified Attack for Omni-Modality Person Re-Identification

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IEEE Transactions on information Forensics and Security 2025年

作者： Bian, Yuan Liu, Min Yi, Yunqi Wang, Xueping Ma, Yunfeng Wang, Yaonan Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan Changsha China Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Changsha China

Deep learning based person re-identification (re-id) models have been widely employed in surveillance systems. Recent studies have demonstrated that black-box single-modality and cross-modality re-id models are vulnerable to adversarial examples (AEs), leaving the robustness of multi-modality re-id models unexplored. Due to the lack of knowledge about the specific type of model deployed in the target black-box surveillance system, we aim to generate modality unified AEs for omni-modality (single-, cross- and multi-modality) re-id models. Specifically, we propose a novel Modality Unified Attack method to train modality-specific adversarial generators to generate AEs that effectively attack different omni-modality models. A multi-modality model is adopted as the surrogate model, wherein the features of each modality are perturbed by metric disruption loss before fusion. To collapse the common features of omnimodality models, Cross Modality Simulated Disruption approach is introduced to mimic the cross-modality feature embeddings by intentionally feeding images to non-corresponding modality-specific subnetworks of the surrogate model. Moreover, Multi Modality Collaborative Disruption strategy is devised to facilitate the attacker to comprehensively corrupt the informative content of person images by leveraging a multi modality feature collaborative metric disruption loss. Extensive experiments show that our MUA method can effectively attack the omni-modality re-id models, achieving 55.9%, 24.4%, 49.0% and 62.7% mean mAP Drop Rate, respectively. © 2005-2012 IEEE.

关键词： Modality unified attack Multi Modality Collaborative Disruption Omni-Modality re-id

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Knowledge Graph Entity Typing with Curriculum Contrastive Learning 31

Knowledge Graph Entity Typing with Curriculum Contrastive Le...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wang, Hao Nuo, Minghua Jiang, Shan College of Computer Science Inner Mongolia University Inner Mongolia Hohhot010021 China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Hohhot010021 China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Inner Mongolia Hohhot010021 China

ISBN: (纸本)9798891761964

The Knowledge Graph Entity Typing (KGET) task aims to predict missing type annotations for entities in knowledge graphs. Most recent studies only focus on the structural information from an entity's neighborhood or semantic information from textual representations of entities or relations. In this paper, inspired by curriculum learning and contrastive learning, we propose the CCLET model using the Curriculum Contrastive Learning strategy for KGET, which uses the Pre-trained Language Model (PLM) and the graph model to fuse the entity related semantic and the structural information of the Knowledge Graph (KG) respectively. Our CCLET model consists of two main parts. In the Knowledge Fusion part, we design an Enhanced-MLP architecture to fuse the text of the entity's description, related triplet and tuples;In the Curriculum Contrastive Learning part, we define the difficulty of the training course by controlling the level of added noise, we aim to accurately learn with curriculum contrastive learning strategy from easy to difficult. Our extensive experiments demonstrate that the CCLET model outperforms recent state-of-the-art models, verifying its effectiveness in the KGET task. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

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A hybrid feature selection method based on novel binary emperor penguin optimization for IoT intrusion detection system 3

A hybrid feature selection method based on novel binary empe...

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3rd International Conference on Algorithms, Network, and Communication Technology, ICANCT 2024

作者： Liu, Xinqian Sun, Shengting Xu, Benda Feng, Zhiquan School of Computer Science and Technology Shandong University of Technology Shandong255000 China Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing Shandong250022 China Finance Office Tianjin University of Traditional Chinese Medicine Tianjin301617 China School of Information Science and Engineer University of Jinan Shandong250022 China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

Due to the limited resources of IoT devices, massive redundant traffic features result in IoT-IDSs facing higher resource and time consumption. Therefore, this paper proposes a hybrid feature selection method RFI-BEPO to effectively select the least and useful features. Firstly, the gini index contribution value of each feature is calculated. The top 50% features with high contribution values are retained as initial important features. On this basis, a novel binary emperor penguin optimization algorithm (BEPO) is designed for select optimal features. BEPO algorithm utilizes S-shaped function and its variants to find effective solutions in the binary search space. The distance between other emperor penguins and the optimal penguin is recalculated by the idea of the cosine similarity. Then, random forest model is as the classifier to detect abnormal traffic. Finally, RFI-BEPO method is evaluated on latest ToN-IoT and N-BaIoT datasets. Compared with multiple feature selection methods, RFI-BEPO method outperforms the existing methods in feature number, and has great anomaly detection effect. © 2025 SPIE.

关键词： Random forests

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Optimization algorithm for global k-means algorithm

Optimization algorithm for global k-means algorithm

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International Conference on intelligent Control and information processing (ICICIP)

作者： Sen Li Fang Li Xiaonan Luo School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Center for Applied Mathematics of Guangxi (GUET) Guilin China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

The k-means method is widely utilized for clustering. Its simplicity, efficacy, and swiftness make it a favored choice among clustering algorithms. It faces the challenge of sensitivity to the initial class center. The enhancement strategy for the k-means algorithm focuses on refining the initial selection of cluster centroids, no longer using its random selection of initial class center points, but starting from the entire dataset, choosing the most distant point to serve as the starting cluster centroid. This can also prevent the local optimization problem of the k-means algorithm. The improved algorithm proposed in this article is an effective method that can select the initial class center point from a global perspective and reduce computation time. The suggested approach has undergone rigorous testing and comparative analysis across multiple datasets, yielding commendable outcomes.

关键词： Sensitivity Stability criteria Refining Neural networks key performance indicator Clustering algorithms Classification algorithms Optimization Testing intelligent control

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Identity and Modality Attributes Driven Multimodal Fusion Networks for Emotion Recognition in Conversations

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IEEE Transactions on Multimedia 2025年

作者： Shi, Wuzhen Chen, Xuping Yao, Biyun Wen, Yang Sheng, Bin Shenzhen University Guangdong Province Engineering Laboratory for Digital Creative Technology Guangdong Provincial Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen518060 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China

Emotion recognition in conversations (ERC) is a crucial aspect of human-computer interaction and plays an important role in various domains, including healthcare, entertainment, and education. Since the conversation data in the form of multimodal sequences is well suited to be constructed into graphs, the methods based on graph convolutional network (GCN) show incomparable advantages. However, existing methods attempt to model the highly uncertain emotional relationships between different speakers, which is not an easy task and may even introduce interference information. Therefore, we propose an identity and modality attributes driven multimodality fusion network (dubbed IMDNet) for emotion recognition in conversations. Specifically, we construct a speaker-centric graph that only connects nodes of the same speaker within modalities to each other, reducing the interference between the emotions of different speakers. We also introduce the attribute embedding mechanism, which facilitates the correct calculation of correlations between nodes for better multimodal feature fusion. Considering that the emotional correlation between utterances will decrease over time, we present an utterance distance attention to make the fusion network pay more attention to the adjacent utterances. Furthermore, we explore the solution to the data imbalance problem suitable for conversation scenarios. Given the presence of possible anomalous samples in the dataset, we opt for the BoundaryFocalLoss. Experiments on the IEMOCAP and MELD datasets show that our IMDNet outperforms the state-of-the-art methods. © 1999-2012 IEEE.

关键词： Convolution

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Improving Event-Level Financial Sentiment Analysis with Retrieval-Augmented Multipath Chain-of-Thought Prompting 12th

Improving Event-Level Financial Sentiment Analysis with Ret...

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12th CCF Conference on BigData, BigData 2024

作者： Zhang, Yiming Ao, Xiang Yu, Guoxin He, Qing Henan Institute of Advanced Technology Zhengzhou University Zhengzhou450002 China Beijing100190 China Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing100190 China

ISBN: (纸本)9789819610235

Event-level Financial Sentiment Analysis (EFSA) aims to extract all the quintuples containing five sentiment elements from a given financial news text, which has gained prominence as an emerging domain recently. The present study utilizes a 4-hop Chain-of-Thought (CoT) prompting based on LLMs to predict sentiment elements in a fixed order, which neglects the interdependencies among the sentiment elements within a quintuple. Inspired by recent multi-view prompting (MvP) and CoT ideas, we propose a novel framework termed Retrieval-Augmented Multipath Chain-of-Thought (RMP-CoT) that aggregates quintuples generated by LLMs through different reasoning paths, leveraging a retrieval-augmented mechanism. Specifically, RMP-CoT integrates different element orders into CoT prompting to guide LLMs in generating multiple sentiment quintuples through the utilization of retrieval-augmented mechanism, and then selects the most plausible quintuples by voting. To investigate the effectiveness of our framework, we conduct extensive experiments on four benchmark tasks of EFSA. RMP-CoT pushes the state-of-the-art by over 6% F1 on the EFSA task and also performs quite effectively on the other sub-tasks of EFSA. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Chain-of-Thought Event-level Sentiment Analysis Financial Sentiment Analysis

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RobNODDI: Robust NODDI Parameter Estimation with Adaptive Sampling Under Continuous Representation 15th

RobNODDI: Robust NODDI Parameter Estimation with Adaptive S...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2024

作者： Xiao, Taohui Cheng, Jian Fan, Wenxin Yang, Jing Li, Cheng Dong, Enqing Wang, Shanshan School of Mechanical Electrical and Information Engineering Shandong University Weihai264209 China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Zhejiang Hangzhou China Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Peng Cheng Laboratory Guangdong Shenzhen China

ISBN: (纸本)9783031869198

Neurite Orientation Dispersion and Density Imaging (NODDI) is an important imaging technology used to evaluate the microstructure of brain tissue, which is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-based methods perform parameter estimation through diffusion magnetic resonance imaging (dMRI) with a small number of diffusion gradients. These methods speed up parameter estimation and improve accuracy. However, the diffusion directions used by most existing deep learning models during testing needs to be strictly consistent with the diffusion directions during training. This results in poor generalization and robustness of deep learning models in dMRI parameter estimation. In this work, we first verify that the parameter estimation performance of current mainstream methods will significantly decrease when the testing diffusion directions and the training diffusion directions are inconsistent. A robust NODDI parameter estimation method with adaptive sampling under continuous representation (RobNODDI) is proposed. Furthermore, long short-term memory (LSTM) units and fully connected layers are selected to learn continuous representation signals. To this end, we use a total of 100 subjects to conduct experiments based on the Human Connectome Project (HCP) dataset, of which 60 are used for training, 20 are used for validation, and 20 are used for testing. The test results indicate that RobNODDI improves the generalization performance and robustness of the deep learning model, enhancing the stability and flexibility of deep learning NODDI parameter estimatimation applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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