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arXiv 2024年

作者： Liu, Chenyang Chen, keyan Zhang, Haotian Qi, Zipeng Zou, Zhengxia Shi, Zhenwei The Image Processing Center School of Astronautics Beihang University Beijing100191 China The Beijing Key Laboratory of Digital Media Beihang University Beijing100191 China The State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China The Shanghai Artificial Intelligence Laboratory Shanghai200232 China Shen Yuan Honors College of Beihang University Beijing100191 China The Department of Guidance Navigation and Control School of Astronautics Beihang University Beijing100191 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China

Monitoring changes in the Earth’s surface is crucial for understanding natural processes and human impacts, necessitating precise and comprehensive interpretation methodologies. Remote sensing satellite imagery offers a unique perspective for monitoring these changes, leading to the emergence of remote sensing image change interpretation (RSICI) as a significant research focus. Current RSICI technology encompasses change detection and change captioning, each with its limitations in providing comprehensive interpretation. To address this, we propose an interactive Change-Agent, which can follow user instructions to achieve comprehensive change interpretation and insightful analysis, such as change detection and change captioning, change object counting, change cause analysis, etc. The Change-Agent integrates a multi-level change interpretation (MCI) model as the eyes and a large language model (LLM) as the brain. The MCI model contains two branches of pixel-level change detection and semantic-level change captioning, in which the BI-temporal Iterative Interaction (BI3) layer is proposed to enhance the model’s discriminative feature representation capabilities. To support the training of the MCI model, we build the LEVIR-MCI dataset with a large number of change masks and captions of changes. Experiments demonstrate the SOTA performance of the MCI model in achieving both change detection and change description simultaneously, and highlight the promising application value of our Change-Agent in facilitating comprehensive interpretation of surface changes, which opens up a new avenue for intelligent remote sensing applications. © 2024, CC BY.

关键词： Change detection

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Research and application of Transformer based anomaly detection model: A literature review

arXiv

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arXiv 2024年

作者： Ma, Mingrui Han, Lansheng Zhou, Chunjie Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan430074 China The Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China

Transformer, as one of the most advanced neural network models in Natural Language processing (NLP), exhibits diverse applications in the field of anomaly detection. To inspire research on Transformer-based anomaly detection, this review offers a fresh perspective on the concept of anomaly detection. We explore the current challenges of anomaly detection and provide detailed insights into the operating principles of Transformer and its variants in anomaly detection tasks. Additionally, we delineate various application scenarios for Transformer-based anomaly detection models and discuss the datasets and evaluation metrics employed. Furthermore, this review highlights the key challenges in Transformer-based anomaly detection research and conducts a comprehensive analysis of future research trends in this domain. The review includes an extensive compilation of over 100 core references related to Transformer-based anomaly detection. To the best of our knowledge, this is the first comprehensive review that focuses on the research related to Transformer in the context of anomaly detection. We hope that this paper can provide detailed technical information to researchers interested in Transformer-based anomaly detection tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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InferEM: Inferring the Speaker’s Intention for Empathetic Dialogue Generation

arXiv

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arXiv 2022年

作者： Lv, Guoqing Li, Jiang Wang, Xiaoping Zeng, Zhigang The School of Artificial Intelligence and Automation The Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China The Hubei Key Laboratory of Brain-inspired Intelligent Systems Huazhong University of Science and Technology Wuhan430074 China

Current approaches to empathetic response generation typically encode the entire dialogue history directly and put the output into a decoder to generate friendly feedback. These methods focus on modelling contextual information but neglect capturing the direct intention of the speaker. We argue that the last utterance in the dialogue empirically conveys the intention of the speaker. Consequently, we propose a novel model named InferEM for empathetic response generation. We separately encode the last utterance and fuse it with the entire dialogue through the multi-head attention based intention fusion module to capture the speaker’s intention. Besides, we utilize previous utterances to predict the last utterance, which simulates human’s psychology to guess what the interlocutor may speak in advance. To balance the optimizing rates of the utterance prediction and response generation, a multitask learning strategy is designed for InferEM. Experimental results demonstrate the plausibility and validity of InferEM in improving empathetic expression. Copyright © 2022, The Authors. All rights reserved.

关键词： Encoding (symbols)

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MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective state Space Modeling

arXiv

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arXiv 2024年

作者： Liu, Zili Chen, Hao Bai, Lei Li, Wenyuan Ouyang, Wanli Zou, Zhengxia Shi, Zhenwei The Image Processing Center School of Astronautics The Beijing Key Laboratory of Digital Media Beihang University Beijing100191 China The Shanghai Artificial Intelligence Laboratory Shanghai200232 China The Department of Geography University of Hong Kong Hong Kong The Department of Guidance Navigation and Control School of Astronautics Beihang University Beijing100191 China

In an era of frequent extreme weather and global warming, obtaining precise, fine-grained near-surface weather forecasts is increasingly essential for human activities. Downscaling (DS), a crucial task in meteorological forecasting, enables the reconstruction of high-resolution meteorological states for target regions from global-scale forecast results. Previous downscaling methods, inspired by CNN and Transformer-based super-resolution models, lacked tailored designs for meteorology and encountered structural limitations. Notably, they failed to efficiently integrate topography, a crucial prior in the downscaling process. In this paper, we address these limitations by pioneering the selective state space model into the meteorological field downscaling and propose a novel model called MambaDS. This model enhances the utilization of multivariable correlations and topography information, unique challenges in the downscaling process while retaining the advantages of Mamba in long-range dependency modeling and linear computational complexity. Through extensive experiments in both China mainland and the continental United states (CONUS), we validated that our proposed MambaDS achieves state-of-the-art results in three different types of meteorological field downscaling settings. We will release the code subsequently. Copyright © 2024, The Authors. All rights reserved.

关键词： Weather forecasting

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Facial Affect Analysis: Learning from Synthetic Data & Multi-Task Learning Challenges

arXiv

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arXiv 2022年

作者： Li, Siyang Xu, Yifan Wu, Huanyu Wu, Dongrui Yin, Yingjie Cao, Jiajiong Ding, Jingting Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Ant Group Hangzhou China

Facial affect analysis remains a challenging task with its setting transitioned from lab-controlled to in-the-wild situations. In this paper, we present novel frameworks to handle the two challenges in the 4th Affective Behavior Analysis In-The-Wild (ABAW) competition: i) Multi-Task-Learning (MTL) Challenge and ii) Learning from Synthetic Data (LSD) Challenge. For MTL challenge, we adopt the SMM-EmotionNet with a better ensemble strategy of feature vectors. For LSD challenge, we propose respective methods to combat the problems of single labels, imbalanced distribution, fine-tuning limitations, and choice of model architectures. Experimental results on the official validation sets from the competition demonstrated that our proposed approaches outperformed baselines by a large margin. The code is available at https://***/sylyoung/ABAW4-HUST-ANT. © 2022, CC BY-NC-ND.

关键词： Face recognition

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Simulation-based Method for Determing Parameters of Material Price Adjustment Model in Large-scale Hydropower Development in China

Simulation-based Method for Determing Parameters of Material...

引用

2021智慧城市与智能建造高端论坛

作者： Lijing Yang Zhenyuan Liu Xi Chen Yaodi Fan Hualin Chen School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Education Ministry for Image Processing and Intelligent Control Yalong River Hydropower Development Company LTD

In the construction of large-scale hydropower projects in China,enormous amounts of materials are *** by various factors,fluctuation of materials price is very common during the *** protect the benefits of the demander and the supplier,reasonable material price adjustment methods need to be set in the procurement bidding documents.A price adjustment model is established considering price fluctuation stratifying and risk sharing.A simulation-based method is proposed for determing parameters in the model,and the simulation process is optimized with the optimal computing budget allocation *** on the judgment of engineers and real data of a hydropower project in Sichuan,China,and simulation cases are generated and the experimental results are analyzed with the *** results show that the simulation-based method provides scientific and reasonable support for determing parameters of material price adjustment model,and the optimal computing budget allocation algorithm plays an optimizing role.

关键词： Large-scale Hydropower Development Material Price Adjustment Parameters Determing Simulation Optimal Computing Budget Allocation TOPSIS

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Watch the Speakers: A Hybrid Continuous Attribution Network for Emotion Recognition in Conversation With Emotion Disentanglement

Watch the Speakers: A Hybrid Continuous Attribution Network ...

引用

International Conference on Tools for Artificial intelligence (ICTAI)

作者： Shanglin Lei Xiaoping Wang Guanting Dong Jiang Li Yingjian Liu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Institute of Artificial Intelligence Huazhong University of Science and Technology (HUST) Wuhan China

Emotion Recognition in Conversation (ERC) has attracted widespread attention in the natural language processing field due to its enormous potential for practical applications. Existing ERC methods face challenges in achieving generalization to diverse scenarios due to insufficient modeling of context, ambiguous capture of dialogue relationships and overfitting in speaker modeling. In this work, we present a Hybrid Continuous Attributive Network (HCAN) to address these issues in the perspective of emotional continuation and emotional attribution. Specifically, HCAN adopts a hybrid recurrent and attention-based module to model global emotion continuity. Then a novel Emotional Attribution Encoding (EAE) is proposed to model intra- and inter-emotional attribution for each utterance. Moreover, aiming to enhance the robustness of the model in speaker modeling and improve its performance in different scenarios, A comprehensive loss function emotional cognitive loss $\mathcal{L}_{EC}$ is proposed to alleviate emotional drift and overcome the overfitting of the model to speaker modeling. Our model achieves state-of-the-art performance on three datasets, demonstrating the superiority of our work. Another extensive comparative experiments and ablation studies on three benchmarks are conducted to provided evidence to support the efficacy of each module. Further exploration of generalization ability experiments shows the plug-and-play nature of the EAE module in our method.

关键词：

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Watch the Speakers: A Hybrid Continuous Attribution Network for Emotion Recognition in Conversation With Emotion Disentanglement

arXiv

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arXiv 2023年

作者： Lei, Shanglin Wang, Xiaoping Dong, Guanting Li, Jiang Liu, Yingjian School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Education Ministry of China Wuhan430074 China Wuhan430074 China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China

Emotion Recognition in Conversation (ERC) has attracted widespread attention in the natural language processing field due to its enormous potential for practical applications. Existing ERC methods face challenges in achieving generalization to diverse scenarios due to insufficient modeling of context, ambiguous capture of dialogue relationships and overfitting in speaker modeling. In this work, we present a Hybrid Continuous Attributive Network (HCAN) to address these issues in the perspective of emotional continuation and emotional attribution. Specifically, HCAN adopts a hybrid recurrent and attention-based module to model global emotion continuity. Then a novel Emotional Attribution Encoding (EAE) is proposed to model intra- and inter-emotional attribution for each utterance. Moreover, aiming to enhance the robustness of the model in speaker modeling and improve its performance in different scenarios, A comprehensive loss function emotional cognitive loss LEC is proposed to alleviate emotional drift and overcome the overfitting of the model to speaker modeling. Our model achieves state-of-the-art performance on three datasets, demonstrating the superiority of our work. Another extensive comparative experiments and ablation studies on three benchmarks are conducted to provided evidence to support the efficacy of each module. Further exploration of generalization ability experiments shows the plug- and-play nature of the EAE module in our method. © 2023, CC BY-SA.

关键词： Emotion Recognition

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Pcda: Efficient Decision-Based Black-Box Attack for Point Clouds

SSRN

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SSRN 2023年

作者： Liu, Lianguang Lv, Junting Li, Depeng Xu, Bingrong Chen, Chongyang Zeng, Zhigang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China School of Automatio Wuhan University of Technology Wuhan430074 China

Deep neural networks for 3D point clouds have shown significant progress in safety-critical applications. However, the robustness of these deep 3D models is insufficient-explored when faced with adversarial attacks. The existing point clouds attack methods usually rely on detailed model information (white-box attacks) or access to the training data (transfer-based black-box attacks). In this work, we make the first attempt in decision-based black-box attack for point clouds (PCDA), which only leverages the predicted labels of the victim models. Specifically, a novel perturbation projection based on chamfer distance is developed to be compatible with 3D irregular data, and the gradient estimation based on low-dimensional subspaces can generate smoother perturbations. Moreover, armed with iterative closest point, the novel perturbation initialization can improve the effectiveness of targeted black-box attacks. Evaluated on a variety of typical 3D models, our algorithm can efficiently generate smooth 3D adversarial samples with a lower magnitude of perturbations. © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Cooperative Label-Free Moving Target Fencing for Second-Order Multi-Agent Systems with Rigid Formation

arXiv

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arXiv 2023年

作者： Hu, Bin-Bin Zhang, Hai-Tao Shi, Yang School of Artificial Intelligence Automation Key Laboratory of Image Processing Intelligent Control State Key Lab of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Mechanical Engineering University of Victoria VictoriaBCV8W 2Y2 Canada

This paper proposes a label-free controller for a second-order multi-agent system to cooperatively fence a moving target of variational velocity into a convex hull formed by the agents whereas maintaining a rigid formation. Therein, no label is predetermined for a specified agent. To attain a rigid formation with guaranteed collision avoidance, each controller consists of two terms: a dynamic regulator with an internal model to drive agents towards the moving target merely by position information feedback, and a repulsive force between each pair of adjacent agents. Significantly, sufficient conditions are derived to guarantee the asymptotic stability of the closed-loop systems governed by the proposed fencing controller. Rigorous analysis is provided to eliminate the strong nonlinear couplings induced by the label-free property. Finally, the effectiveness of the controller is substantiated by numerical simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi agent systems

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