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arXiv 2025年

作者： Li, Siyang Wang, Hongbin Chen, Xiaoqing Wu, Dongrui The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Henan Key Laboratory of Brain Science and Brain Computer Interface Technology School of Electrical and Information Engineering Zhengzhou University China

Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices. This review highlights the core decoding algorithms that enable multimodal BCIs, including a dissection of the elements, a unified view of diversified approaches, and a comprehensive analysis of the present state of the field. We emphasize algorithmic advancements in cross-modality mapping, sequential modeling, besides classic multi-modality fusion, illustrating how these novel AI approaches enhance decoding of brain data. The current literature of BCI applications on visual, speech, and affective decoding are comprehensively explored. Looking forward, we draw attention on the impact of emerging architectures like multimodal Transformers, and discuss challenges such as brain data heterogeneity and common errors. This review also serves as a bridge in this interdisciplinary field for experts with neuroscience background and experts that study AI, aiming to provide a comprehensive understanding for AI-powered multimodal BCIs. © 2025, CC BY-NC-ND.

关键词： Brain computer interface

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Improving Transferability of Adversarial Point Clouds with Model Commonalities

Improving Transferability of Adversarial Point Clouds with M...

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IEEE International Conference on Cyborg and Bionic Systems (CBS)

作者： Junting Lv Lianguang Liu Yutong Zhang Depeng Li Zhigang Zeng 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

Although deep learning of 3D point clouds has made significant progress, the robustness of 3D models has not been fully investigated. Existing 3D attack methods show satisfactory performance under the white-box setting but frequently suffer from a low transferability to attack black-box models. A key point to improve the transferability of attacks is to find the commonalities of multiple models. To this end, a novel attack method is proposed by only leveraging perturbations in local geometric feature areas termed FAP. Specifically, local perturbations are pertinently added by calculating the curvature to sample more points in the feature areas that 3D models potentially focus on. Moreover, to address the drawback of poor isometric robustness common to 3D models, we propose a generalized framework for diversifying input samples by adding random isometric transformations (DIT) in the attack process, which can be combined with other attack methods to improve their transferability. Evaluated on a variety of typical 3D models, the proposed attack method outperforms the current gradient-based methods in transferable black-box attacks, and the effectiveness of the proposed framework is demonstrated by extending it to other attack methods.

关键词： Point cloud compression Solid modeling Three-dimensional displays Shape Perturbation methods Closed box Focusing

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Evolutionary attraction–repulsion algorithm embedded with LLM for UAV task allocation

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Advanced Engineering Informatics 2025年 66卷

作者： Wu, Bowen Xiao, Renbin School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Institute of Artificial Intelligence Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan430074 China

Labor division provides an adaptive and scalable technique for unmanned systems. However, its designing process usually relies heavily on the manually crafted paradigms, leading to inefficiencies and insufficient performance. To overcome these challenges, this paper introduce an evolutionary approach to labor division algorithm design. Drawing inspiration from the regulatory behaviors observed in natural lion populations, we propose a bipolar feedback-guided attraction–repulsion mechanism. A formal model is developed to encapsulate this mechanism, and we provide an in-depth analysis of the algorithm's evolution through the framework of systems science. In addition, we leverage prompt engineering techniques to integrate a large language model (LLM) with our proposed mechanism, facilitating a bidirectional enhancement between the two. This integration culminates in the development of the ARE-LLM framework, a robust and adaptive solution for designing labor division algorithms. To demonstrate the practical applicability of our approach, we apply the ARE-LLM framework to the UAV-based task allocation problem for enemy air defense suppression. The experimental results indicate that our method outperforms several advanced approaches. In a scenario involving 50 UAVs tasked with completing 300 assignments, the total distance traveled is only 9 % of that required by the ALPDA algorithm, while the processing time is reduced to 8 % of that used by ALPDA. © 2025 Elsevier Ltd

关键词： Target drones

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Hierarchical Spatio-temporal Decoupling for Text-to-Video Generation

arXiv

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arXiv 2023年

作者： Qing, Zhiwu Zhang, Shiwei Wang, Jiayu Wang, Xiang Wei, Yujie Zhang, Yingya Gao, Changxin Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Alibaba Group China Fudan University China

Despite diffusion models having shown powerful abilities to generate photorealistic images, generating videos that are realistic and diverse still remains in its infancy. One of the key reasons is that current methods intertwine spatial content and temporal dynamics together, leading to a notably increased complexity of text-to-video generation (T2V). In this work, we propose HiGen, a diffusion modelbased method that improves performance by decoupling the spatial and temporal factors of videos from two perspectives, i.e., structure level and content level. At the structure level, we decompose the T2V task into two steps, including spatial reasoning and temporal reasoning, using a unified denoiser. Specifically, we generate spatially coherent priors using text during spatial reasoning and then generate temporally coherent motions from these priors during temporal reasoning. At the content level, we extract two subtle cues from the content of the input video that can express motion and appearance changes, respectively. These two cues then guide the model's training for generating videos, enabling flexible content variations and enhancing temporal stability. Through the decoupled paradigm, HiGen can effectively reduce the complexity of this task and generate realistic videos with semantics accuracy and motion stability. Extensive experiments demonstrate the superior performance of HiGen over the state-of-the-art T2V methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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ARShape-Net: Single-View image Oriented 3D Shape Reconstruction with an Adversarial Refiner 1st

ARShape-Net: Single-View Image Oriented 3D Shape Reconstruct...

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1st CAAI International Conference on Artificial Intelligence, CICAI 2021

作者： Xu, Hao Bai, Jing North Minzu University Ningxia Yinchuan750021 China The Key Laboratory of Images and Graphics Intelligent Processing of State Ethnic Affairs Commission Ningxia Yinchuan750021 China

ISBN: (纸本)9783030930455

In this paper, we propose a novel method of reconstructing 3D shapes from single-view images based on an adversarial refiner. Generative Adversarial mechanism is adopted between the coarse-volumes generation stage and the refinement stage. In the coarse-volumes generation stage, a Context Aware Channel Attention Module (CA-CAM) and a VoxFocal Loss are designed to reconstruct the coarse-volume shapes as complete as possible. Furthermore, an adversarial refiner is proposed to adaptively optimize the coarse-volume shapes and remove redundant voxels by adding the discriminator into refiner. On the challenging large synthesis-image dataset ShapeNet and the real-world dataset Pix3D, ARShape-Net demonstrated significant quantitative and qualitative improvements compared with the state-of-the-art methods. © 2021, Springer Nature Switzerland AG.

关键词： image enhancement

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SpliceCannon: A novel framework for the prediction of canonical and non-canonical splice sites based on deep learning

SpliceCannon: A novel framework for the prediction of canoni...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yanju Zhang Wangjing Qi Ruohan Lin Jinli Fan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China College of Computer Science and Technology Huaqiao University Xiamen China

Accurately predicting splice sites in DNA sequences is not only of utmost importance in the study of gene expression and protein synthesis, but also crucial to precisely understanding the pathogenesis of various diseases related to splicing. In the past few decades, several tools for identifying splice sites have been developed, however, many of them ignore the contextual influence in DNA sequences in the process of encoding, consequently leading to considerable false positives in prediction. Additionally, most methods solely investigate canonical splicing whereas pay little attention to non-canonical ones. To address the issues mentioned above, we propose a comprehensive method, SpliceCannon, for accurately predicting both canonical and non-canonical splice sites based on neural networks. This method uses the combination of k-nucleotide frequencies and Doc2vec to encode the sequence, then employs a series of comprehensive neural networks such as bidirectional long short-term memory networks and residual networks to effectively extract features from the original sequence while avoiding the problems such as gradient vanishing. The results demonstrate that our method consistently achieves high accuracy when tested on multiple species. Compared to other recent methods, our model exhibits superior performance, stronger generalization ability, and wider applicability. In particular, The recognition accuracy is also remarkably high on the non-canonical dataset. We envision that this proposed method will serve as a promising tool for splice sites prediction to facilitate genomic research.

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Short-Term Smart Grid Load Forecasting Based on CNN-BiLSTM with Attention Mechanism

Short-Term Smart Grid Load Forecasting Based on CNN-BiLSTM w...

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Energy, Power and Grid (ICEPG), International Conference on

作者： Feihu Sun Yanru Zhong School of Computer Science and Information Security Guilin University of Electronic Technology Guilin Guangxi China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin Guangxi China

ISBN: (数字)9798350377798

ISBN: (纸本)9798350377804

The existing power load data exhibits complex multidimensionality, while existing techniques often focus on learning and forecasting from single load data. To fully exploit the complex multidimensionality of the existing power load data and enhance the accuracy of short-term load forecasting, a smart grid short-term load forecasting model based on attention mechanism and CNN-BiLSTM is proposed. Firstly, the one-dimensional convolutional network (1D-CNN) slides along the temporal dimension to extract multidimensional features such as temperature and humidity from the raw power load data. Secondly, the Bidirectional Long Short-Term Memory network (BiLSTM) captures both historical and future information of the data through gate mechanisms and the bidirectional network structure. Lastly, the attention mechanism allocates weights to focus on important parts of the load sequence data, enhancing the model’s ability to identify key information and further improving the accuracy of short-term power load forecasting. The method is experimented on a real-world multivariate load dataset and compared with existing learning methods. The proposed model achieved lower RMSE in 24-hour load forecasting compared to CNN-LSTM, CNN-BiLSTM, LSTM-Attention, BiLSTM-Attention and CNN-LSTM-Attention models by 13.2%, 10.6%, 6.7%, 5.7% and 5.9% respectively.

关键词： Attention mechanisms Accuracy Load forecasting Electricity Predictive models Feature extraction Data models Smart grids Convolutional neural networks Load modeling

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Selective Bokeh Effect Transformation

Selective Bokeh Effect Transformation

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Juewen Peng Zhiyu Pan Chengxin Liu Xianrui Luo Huiqiang Sun Liao Shen Ke Xian Zhiguo Cao Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology S-Lab Nanyang Technological University

Bokeh effect transformation is a novel task in computer vision and computational photography. It aims to convert bokeh effects from one camera lens to another. To this end, we introduce a new concept of blur ratio, which represents the ratio of the blur amount of a target image to that of a source image, and propose a novel framework SBTNet based on this concept. For cat-eye simulation and lens type transformation, a two-channel coordinate map and a two-channel one-hot map are added as extra inputs. The core of the framework is a sequence of parallel FeaNets, along with a feature selection and integration strategy, which aims to transform the blur amount with arbitrary blur ratio. The effectiveness of the proposed framework is demonstrated through extensive experiments, and our solution has achieved the top LPIPS metric in NTIRE 2023 Bokeh Effect Transformation Challenge.

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Multi-Scale Visual Semantic Enhanced for Multi-Modal Ner

SSRN

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SSRN 2023年

作者： Wang, Hairong Xu, Xi Wang, Tong Sun, Chong School of Computer Science and Engineering North Minzu University Ningxia750021 China The Key Laboratory of Images Graphics Intelligent Processing of State Ethnic Affairs Commission Ningxia750021 China

A series of methods using image semantics to assist in extracting named entity recognition have been proposed recently. However, in these multi-modal named entity recognition methods, there may be problems of visual semantic missing and weak semantic constraints in multi-modal representations. For this reason, we propose a multi-scale visual semantic enhanced for multi-modal named entity recognition method. This method uses a variety of visual features for collaborative representation to obtain more complete visual semantics, integrates semantic information in multi-scale visual features through the multi-modal feature fusion module, to generate multi-scale visual semantic features, and transmits its semantics to text features to obtain semantically enhanced multi-modal text representations. The visual entity perceiver is used to semantically constrain the multi-scale visual semantic features to learn consistent visual semantics. The multi-task label decoder is called to jointly mine the fine-grained entity semantics of the multi-modal text representation and text features, to enhance the semantic accuracy of the prediction features, and solve the problem of semantic bias, thereby further improving the accuracy of named entity recognition. In the two multi-modal named entity recognition datasets (Twitter-2015, Twitter-2017), compared with 10 methods such as HvpNet, MNER-QG, RGCN, etc., the average F1 value of this method increased by 0.85% and 1.45% respectively. In addition, visualization and comparative analysis were conducted on the recognition results, visual features, and text features of methods such as MSVSE, UMT, MAF, and HvpNet in different multimodal data scenarios with varying entity types and quantities. This further validated the effectiveness of the text-based methods. © 2023, The Authors. All rights reserved.

关键词： Semantics

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Solving Nonlinear Equations Systems with a Two-Phase Root-Finder Based on Niching Differential Evolution 15th

Solving Nonlinear Equations Systems with a Two-Phase Root-Fi...

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15th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2020

作者： Duan, Lianlang Xu, Fei Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789811613531

Solving nonlinear equation systems (NESs) is a challenging problems in numerical computation. Two goals should be considered for solving NESs. One is to locate as many roots as possible and the other is to improve the solving precision. In this work, in order to achieve these two goals, a two-phase niching differential evolution (TPNDE) is proposed. The innovation of TPNDE is mainly reflected in the following three aspects. First, a probabilistic mutation mechanism is designed to increase the diversity of the population. Second, a novel reinitialization procedure is proposed to detect converged individuals and explore more new regions that may contain global optima. Third, distribution adjustment local search (DALS) is proposed to deal with the isolated solution and enhance the convergence of the algorithm. To evaluate the performance of TPNDE, we test our TPNDE and 5 state-of-the-art algorithms on 20 NESs with diverse features. The experimental results show that the proposed TPNDE algorithm is better than the other methods. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Nonlinear equations

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