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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Tianlong Tan Bin Chen Hongliang Cao Chenggang Yan Yike Ma Feng Dai Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences Beijing China Shandong University University of Chinese Academy of Sciences Beijing China Hangzhou Dianzi University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Self-Supervised Semantic Segmentation, aiming to leverage masses of unlabeled data for boosting semantic segmentation, has been rapidly emerging as an active task in recent years. However, existing self-supervised semantic segmentation approaches mainly focus on planar images, leaving multiple distorted objects encountered in panoramic images unexplored due to the formidable challenge of handling heterogeneous degrees of distortions across different locations. In this paper, we propose a novel Self-Supervised Panoramic Semantic Segmentation model, termed DASC-SPT, built upon the mainstream contrastive learning framework. Towards distortions in panoramic images, we present two structures to better learn from distorted features by applying planar images. For the input images of self-supervision, we design a Spherical Projection Transformation (SPT) strategy that involves randomly projecting planar images onto various locations of the sphere to introduce the distortions. For pixel-wise distorted features, we construct a Deformation-aware Sampling Consistency (DASC) framework to further utilize the shared content and discrepancies caused by different distortions of paired views, where the deformation-aware consistency can be quantified on pixel-wise features. Both of the two components facilitate the model to adapt to distortions and boost panoramic semantic segmentation. Extensive comprehensive experiments on three panoramic datasets demonstrate the effectiveness and superiority of DASC-SPT approach.

关键词： Training Deformable models Adaptation models Computer vision Semantic segmentation Computational modeling Crops Contrastive learning Distortion Boosting

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Privacy-Preserving Image Retrieval Based on Thumbnail-Preserving Visual Features

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： An, Dezhi Zhang, Xudong Hao, Dawei Zhao, Ruoyu Zhang, Yushu Gansu University of Political Science and Law School of Cyber Security Lanzhou730070 China Jiangxi University of Finance and Economics School of Computing and Artificial Intelligence Nanchang330013 China Jiangxi Provincial Key Laboratory of Multimedia Intelligent Processing Nanchang330013 China

Images are generally uploaded to the cloud in plaintext and can be retrieved in the cloud, but privacy may be exposed. To solve this problem, Privacy Preserving Content Based Image Retrieval (PPCBIR) system was proposed. In this system, noise-like image encryption algorithm was used in the early scheme, and Thumbnail Preserving Encryption (TPE) technology was proposed later to balance image privacy and visual usability. However, the existing TPE schemes supporting retrieval have shortcomings in mining the visual usability of TPE images, which limits the retrieval accuracy. Based on this, we propose a VF-PPCBIR scheme combining TPE and image visual features to improve retrieval efficiency and accuracy while ensuring image privacy. Specifically, we redesign a new TPE algorithm for lossless encryption and decryption of arbitrary size images. The design concept of the encryption algorithm is novel, and the encryption effect is more stable. The retrieval process generates thumbnails of the retrieved image and extracts local features in the spatial domain, which are matched with the features extracted from TPE thumbnails in the cloud, and the user can directly select the desired image. In addition, the retrieval scheme uses adjustable feature algorithm to achieve approximate similarity between the ciphertext and the plaintext thumbnail, to achieve accurate feature matching. The experimental results show that the time cost, and mean average precision (mAP) can reach 9.121s and 64.343%, respectively. © 2025 IEEE. All rights reserved.

关键词： Differential privacy

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MFA-LJP: IMPROVED LEGAL JUDGMENT PREDICTION USING BERT EMBEDDINGS-BASED MULTIFRACTAL ANALYSIS

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Fractals 2025年

作者： Guan, Jian-Hua Yu, Zu-Guo Sun, Xin-Gen National Center for Applied Mathematics in Hunan Xiangtan University Hunan 411105 China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan 411105 China

Legal Judgment Prediction (LJP) is the task of automatically predicting judgment results based on case fact descriptions. However, these fact descriptions can be regarded as lengthy and complex chains of events, and traditional text feature extraction methods struggle to capture the complex multi-scale features from them. To address this, we propose a legal judgment prediction model based on multifractal analysis and BERT embeddings (MFA-LJP). In this work, we treat the [CLS] embeddings from BERT as one-dimensional time series and perform multifractal analysis to reveal the multifractal properties that these time series exhibit. Furthermore, for each time series, we extract multifractal parameters and concatenate them with the corresponding output from the BiGRU to create a new feature vector. Finally, this feature vector is used as the input to a fully connected layer for the prediction tasks of applicable law articles, charges, and terms of penalty, respectively. Experiments on CAIL datasets demonstrate the effectiveness of MFA. With only 11 multifractal parameters added, MFA-LJP can achieve considerable performance improvement in three sub-tasks. Compared to the current best baseline model HD-LJP, on the CAIL-small dataset, MFA-LJP improves the F1 scores for applicable law articles, charges, and terms of penalty by 5.63%, 2.54%, and 2.95%, respectively. Similarly, on the CAIL-big dataset, MFA-LJP also shows notable achievements, with F1 scores for applicable law articles, charges, and terms of penalty improving by 3.08%, 4.33%, and 8.91%, respectively. © 2025 World Scientific Publishing Company.

关键词： BERT Embedding Legal Judgment Prediction Multifractal Analysis

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Autonomous Path Planning via Sand Cat Swarm Optimization With Multi-Strategy Mechanism for Unmanned Aerial Vehicles in Dynamic Environment

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IEEE Internet of Things Journal 2025年

作者： Deng, Wu Feng, Jiayi Zhao, Huimin Civil Aviation University of China College of Electronic Information and Automation Tianjin300300 China Civil Aviation University of China Tianjin Key Lab Intelligent Signal and Image Processing Tianjin300300 China State Key Laboratory of Rail Transit Vehicle System of Southwest Jiaotong University Chengdu610031 China

Unmanned Aerial Vehicles (UAVs) face critical challenges in path planning in dynamic environment. Sand Cat Swarm Optimization (SCSO) algorithm has demonstrated promise in addressing complex optimization challenges. However, SCSO is limited by slow convergence, susceptibility to local optima, and insufficient adaptability. To overcome these shortcomings, an enhanced SCSO with the spiral search, Livy flight, tent chaotic mapping, and adaptive sparrow alert mechanism, namely TSLS-SCSO is developed to propose an autonomous path planning method for UAVs in dynamic environment. In TSLS-SCSO, a new population initialization strategy with tent chaotic mapping is designed to achieve a large dynamic range and coverage capability. Then a new spiral search strategy is designed to broaden the search range in the search phase. And a new Livy flight strategy is employed to enrich the diversity of population in the attacking prey phase. A new sparrow alert mechanism with integrating the sand cat group with the sparrow alert is designed to obtain faster convergence speed and accuracy. The experiment results on CEC 2017 and CEC 2022 show that the TSLS-SCSO obtains higher accuracy and more stable solutions and exhibits better competitiveness. Furthermore, the proposed UAV path planning method successfully found effective paths with an obstacle avoidance effectiveness of 95.5%. The obtained results validate the effectiveness and competitiveness of TSLS-SCSO in UAV path planning in the dynamic environment. © 2014 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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An Encoder-Decoder Model Based On Spiking Neural Networks For Address Event Representation Object Recognition

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Du, Sichun Zhu, Haodi Zhang, Yang Hong, Qinghui Hunan University College of Computer Science and Electronic Engineering Changsha418002 China Shenzhen University Computer Vision Institute School of Computer Science and Software Engineering National Engineering Laboratory for Big Data System Computing Technology Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

Address event representation (AER) object recognition task has attracted extensive attention in neuromorphic vision processing. The spike-based and event-driven computation inherent in the spiking neural network (SNN) provides an energy-saving solution for AER object recognition. However, SNN with spike timing dependent plasticity (STDP) learning rule has not achieved satisfying AER object recognition performance. This work proposes an SNN-based encoder-decoder model to improve the recognition performance of AER objects. An STDP-based locally connected spiking neural network (LC-SNN) is proposed as an encoder to extract rich spatiotemporal features from AER event flows more flexibly. After the encoder extracts and learns primary features, we propose a fully connected spiking neural network (FC-SNN) based on the reward-modulated spike-timing-dependent plasticity (R-STDP) learning rule as a decoder to learn higher-level features for classification. In addition, we improved the winner-take-all (WTA) mechanisms and R-STDP learning rule in the decoder based on the reward and punish decision, enabling the network to perform better. The experiments are performed on the N-MNIST, MNIST-DVS, and the DVS Gesture datasets, improving the accuracy of the best existing plasticity-based SNN by 0.19%, 0.27% and 1.35%, respectively. © 2016 IEEE.

关键词： Decoding

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Total Field and Scattered Field Technique for Fourth-Order Symplectic Finite Difference Time Domain Method

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Chinese Physics Letters 2006年第1期23卷 103-105页

作者：沙威黄志祥吴先良陈明生 Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei 230039

Using symplectic integrator propagator, a three-dimensional fourth-order symplectic finite difference time domain （SFDTD） method is studied, which is of the fourth order in both the time and space domains. The method is nondissipative and can save more memory compared with the traditional FDTD method. The total field and scattered field （TF-SF） technique is derived for the SFDTD method to provide the incident wave source conditions. The bistatic radar cross section （RCS） of a dielectric sphere is computed by using the SFDTD method for the first time. Numerical results suggest that the SFDTD algorithm acquires better stability and accuracy compared with the traditional FDTD method.

关键词： INTEGRATOR SIMULATION STABILITY SCHEME

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AMHF-TP: Multifunctional therapeutic peptides prediction based on multi-granularity hierarchical features

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Quantitative Biology 2025年第1期 44-58页

作者： Shouheng Tuo YanLing Zhu Jiangkun Lin Jiewei Jiang School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an Key Laboratory of Big Data and Intelligent Computing School of Electronic Engineering Xi'an University of Posts and Telecommunications

Multifunctional therapeutic peptides(MFTP) hold immense potential in diverse therapeutic contexts, yet their prediction and identification remain challenging due to the limitations of traditional methodologies, such as extensive training durations, limited sample sizes, and inadequate generalization capabilities. To address these issues, we present AMHF-TP, an advanced method for MFTP recognition that utilizes attention mechanisms and multi-granularity hierarchical features to enhance performance. The AMHF-TP is composed of four key components: a migration learning module that leverages pretrained models to extract atomic compositional features of MFTP sequences; a convolutional neural network and selfattention module that refine feature extraction from amino acid sequences and their secondary structures; a hypergraph module that constructs a hypergraph for complex similarity representation between MFTP sequences; and a hierarchical feature extraction module that integrates multimodal peptide sequence features. Compared with leading methods,the proposed AMHF-TP demonstrates superior precision, accuracy, and coverage, underscoring its effectiveness and robustness in MFTP recognition. The comparative analysis of separate hierarchical models and the combined model, as well as with five contemporary models, reveals AMHF-TP's exceptional performance and stability in recognition tasks.

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Reversible Data Hiding with Flexible Extraction for Thumbnail-Preserving Encryption

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IEEE Transactions on Consumer Electronics 2025年

作者： An, Dezhi Pu, Xin Hao, Dawei Zhao, Ruoyu Zhang, Yushu Gansu University of Political Science and Law School of Cyber Security Gansu Lanzhou730070 China Jiangxi University of Finance and Economics School of Computing and Artificial Intelligence Nanchang330013 China Jiangxi Provincial Key Laboratory of Multimedia Intelligent Processing Nanchang330013 China

Storing images in the cloud not only facilitates consumers management of their images, but also alleviates the storage burden on local consumer electronic devices, a convenience that often comes at the expense of privacy. Traditional image encryption protects image privacy from disclosure, but sacrifices the visual usability of the images. Thumbnail-preserving encryption (TPE) is a recently proposed encryption scheme that harmonizes the privacy and usability of images. However, most existing TPE schemes ignored the non-visual usability of the images, i.e., the ability to embed additional information into the TPE images, which resulted in insufficient usability of the TPE images. To this end, we propose a TPE scheme for flexible extraction of reversible data hiding (RDH), which utilizes the histogram shifting technique to achieve embedding and lossless extraction of information at different stages before and after permutation encryption. Importantly, the encrypted image can be recovered losslessly. Meanwhile, we integrate a chaotic system into the encryption and decryption processes, thereby significantly reducing computational time and enhancing overall efficiency. Experiments show that our proposed scheme not only embeds additional information in TPE images, but also recovers the original images losslessly. © 1975-2011 IEEE.

关键词： chaotic system image encryption recover losslessly Reversible data hiding

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DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-Preserving Talking Face Synthesis

DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Kaijun Deng Dezhi Zheng Jindong Xie Jinbao Wang Weicheng Xie Linlin Shen Siyang Song Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Department of Computer Science University of Exeter

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Accurately synthesizing talking face videos and capturing fine facial features for individuals with long hair presents a significant challenge. To tackle these challenges in existing methods, we propose a decomposed per-embedding Gaussian fields (DEGSTalk), a 3D Gaussian Splatting (3DGS)-based talking face synthesis method for generating realistic talking faces with long hairs. Our DEGSTalk employs Deformable Pre-Embedding Gaussian Fields, which dynamically adjust pre-embedding Gaussian primitives using implicit expression coefficients. This enables precise capture of dynamic facial regions and subtle expressions. Additionally, we propose a Dynamic Hair-Preserving Portrait Rendering technique to enhance the realism of long hair motions in the synthesized videos. Results show that DEGSTalk achieves improved realism and synthesis quality compared to existing approaches, particularly in handling complex facial dynamics and hair preservation. Our code is available at https://***/CVI-SZU/DEGSTalk.

关键词： Hair Training Three-dimensional displays Dynamics signal processing Rendering (computer graphics) Noise measurement Speech processing Faces Videos

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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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