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Prompt-Based Concept Learning for Few-Shot Class-Incremental Learning

IEEE Transactions on Circuits and Systems for Video Technology 2025年第5期35卷 4991-5005页

作者： Li, Shuo Liu, Fang Jiac, Licheng Li, Lingling Chen, Puhua Liu, Xu Ma, Wenping Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xi'an710071 China Xidian University School of Artificial Intelligence Xi'an710071 China International Research Center for Intelligent Perception and Computation Xi'an710071 China Joint International Research Laboratory of Intelligent Perception and Computation Xi'an710071 China

Few-Shot Class-Incremental Learning (FSCIL) faces a huge stability-plasticity challenge due to continuously learning knowledge from new classes with a small number of training samples without forgetting the knowledge of previously seen old classes. To alleviate this challenge, we propose a novel method called Prompt-based Concept Learning (PCL) for FSCIL, which generalizes conceptual knowledge learned from old classes to new classes by simulating human learning capabilities. In our PCL, in the base session, we simultaneously learn common basic concepts from the training data and the class-concept weight of each class in a prompt learning manner, and in each incremental session, class-concept weights between new classes and previously learned basic concepts are learned to achieve incremental learning. Furthermore, in order to avoid catastrophic forgetting, we propose a distribution estimation module to retain feature distributions of previously seen classes and a data replay module to randomly sample features of previously seen classes in incremental sessions. We verify the effectiveness of our PCL on widely used benchmarks, such as miniimageNet, CIFAR-100, and CUB-200. Experimental results show that our PCL achieves competitive results compared with other state-of-the-art methods, especially we achieve an average accuracy of 94.02% across all sessions on the miniimageNet benchmark. © 1991-2012 IEEE.

关键词： Zero-shot learning

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Distributed optimal consensus control for multiagent systems based on event-triggered and prioritized experience replay strategies

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Science China(Information Sciences) 2025年第1期68卷 306-321页

作者： Cuijuan ZHANG Lianghao JI Shasha YANG Xing GUO Huaqing LI Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University College of Electronic and Information Engineering Southwest University

This study uses event-triggered(ET) and reinforcement learning methods to investigate the optimal consensus control problem for cooperative-competitive multiagent systems. It proposes a novel distributed ET control strategy, which relies on a prioritized experience replay(PER) policy. This strategy not only conserves communication resources but also ensures acceptable system performance. To implement the proposed method, actor-critic(AC) dual-structured neural networks(NNs) are used to approximate the value function and control policy. In the AC NNs, the weight estimates for the NNs are updated at the moment of event triggering, resulting in a nonperiodic weight adjustment pattern. This approach decreases the computational cost in comparison with the traditional ET mechanism. The PER-based ET mechanism makes full use of valid historical data and effectively establishes a balance between system performance and communication resource ***, it does not require the following two conditions in most existing studies:(1) requirement of the system dynamics model to be known, and(2) persistent excitation. In addition, Zeno behavior is excluded from this study. Finally, a simulation is conducted to confirm the validity of the suggested approach.

关键词： multiagent systems event-triggered mechanism prioritized experience replay dual-structured neural networks optimal consensus control

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MLMamba: A Mamba-based Efficient Network for Multi-label Remote Sensing Scene Classification

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Du, Ruiqi Tang, Xu Ma, Jingjing Zhang, Xiangrong Jiao, Licheng Xidian University School of Artificial Intelligence Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education China

As a useful remote sensing (RS) scene interpretation technique, multi-label RS scene classification (RSSC) always attracts researchers' attention and plays an important role in the RS community. To assign multiple semantic labels to a single RS image according to its complex contents, the existing methods focus on learning the valuable visual features and mining the latent semantic relationships from the RS images. This is a feasible and helpful solution. However, they are often associated with high computational costs due to the widespread use of Transformers. To alleviate this problem, we propose a Mamba-based efficient network based on the newly emerged state space model called MLMamba. In addition to the basic feature extractor (convolutional neural network and language model) and classifier (multiple perceptrons), MLMamba consists of two key components: a pyramid Mamba and a feature-guided semantic modeling (FGSM) Mamba. Pyramid Mamba uses multi-scale scanning to establish global relationships within and across different scales, improving MLMamba's ability to explore RS images. Under the guidance of the obtained visual features, FGSM Mamba establishes associations between different land covers. Combining these two components can deeply mine local features, multi-scale information, and long-range dependencies from RS images and build semantic relationships between different surface covers. These superiorities guarantee that MLMamba can fully understand the complex contents within RS images and accurately determine which categories exist. Furthermore, the simple and effective structure and linear computational complexity of the state space model ensure that pyramid Mamba and FGSM Mamba will not impose too much computational burden on MLMamba. Extensive experiments counted on three benchmark multi-label RSSC data sets validate the effectiveness of MLMamba. The positive results demonstrate that MLMamba achieves state-of-the-art performance, surpassing existing meth

关键词： Semantics

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SAR target recognition network based on frequency domain covariance matrix and Riemannian manifold

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Geomatica 2025年第1期77卷

作者： Guo, Zhengxi Hou, Biao Yang, Chen Guo, Xianpeng Wu, Zitong Ren, Bo Jiao, Licheng Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Shaanxi Province Xi'an710071 China

Synthetic Aperture Radar (SAR) image target recognition is a critical problem in remote sensing. Traditional deep learning-based methods often overlook the rich information in the frequency domain of SAR images. Existing frequency domain based approaches typically rely on prior knowledge to select different frequency components or guide network learning, failing to fully exploit the information contained in the frequency domain. We believe that knowledge from the macroscopic high-dimensional space needs to be harnessed to learn about the frequency domain. Therefore, in this paper, we propose an innovative SAR target recognition framework known as FDCM-Net (Frequency Domain Covariance Matrix and Riemannian Manifold Network). Our method integrates the frequency domain information of SAR images with the covariance matrix, revealing the complex relationships between different frequency components and delving into the latent features within the frequency domain. Additionally, we explore the geometric properties of the covariance matrix and design an innovative network architecture based on Riemannian manifold theory. This network effectively captures important information from the frequency domain of SAR images in high-dimensional space while preserving the geometric characteristics of the original data and performing classification. We conducted comprehensive evaluations of FDCM-Net using the MSTAR, OpenSARShip 2.0 and FUSAR-Ship 1.0 datasets. Our results demonstrate that FDCM-Net outperforms previous methods based on spatial and frequency domain baselines in terms of classification performance. © 2025 key laboratory of intelligent perception and image understanding of Ministry of Education, Xidian University

关键词： Deep learning

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A 3D Self-Awareness Diffusion Network for Multimodal Classification

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IEEE Transactions on Multimedia 2025年

作者： Ma, Mengru Ma, Wenping Jiao, Licheng Li, Lingling Liu, Xu Liu, Fang Yang, Shuyuan Guo, Yuwei Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education International Research Center for Intelligent Perception and Computation School of Artificial Intelligence Xi'an710071 China

As imaging sensor technology in remote sensing has advanced quickly, multimodal fusion classification has become an important research direction in land cover and urban planning classification tasks. While generative models and image classification have greatly benefited from diffusion models, the present ones primarily concentrate on single-modality-driven diffusion processes. Therefore, this paper presents a 3D self-awareness diffusion network (3DSA-DiffNet) for multispectral (MS) and panchromatic (PAN) image fusion classification, which would make it easier to classify heterogeneous data from various sensors. First, in order to model the relationship between multi-channel spectra and multi-pixel spatial distributions as well as samples, respectively, a spatial-spectral joint denoising network (S2 JD-Net) is proposed. It can incorporate the diffusion process into the neural network to enhance the quality of diffusion features. Secondly, to imitate the brain's spatial-spectral coexistence learning mechanism, this work offers a 3D self-awareness module (3DSA-Module) that can learn the weight of each pixel in 3D space, resulting in extraordinarily high feature representation capabilities. Finally, experimental verification demonstrates that the 3D self-awareness diffusion fusion network driven by brain inspiration outperforms more sophisticated approaches on the Xi'an, Huhhot, and Muufl datasets. © 1999-2012 IEEE.

关键词： Urban planning

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Adaptive Complex Wavelet Informed Transformer Operator

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IEEE Transactions on Multimedia 2025年

作者： Li, Xiaotong Jiao, Licheng Liu, Fang Yang, Shuyuan Zhu, Hao Liu, Xu Li, Lingling Ma, Wenping Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education International Research Center for Intelligent Perception and Computation Joint International Research Laboratory of Intelligent Perception and Computation School of Artificial Intelligence Shaanxi Xi'an710071 China

Visual transformers have achieved great success in representation learning. This is mainly due to efficient token dependency modeling via self-attention. However, the computational burden increases sharply as the input pixels increase. Although recent Fourier-based global frequency-domain mixing methods attempt to improve the efficiency of transformers for high-resolution image inputs, the Fourier operator has limited ability to capture the local geometric structure. Complex wavelets can perform local attention in both the spatial domain and the frequency domain. Therefore, we propose the complex wavelet informed transformer operator that uses the real and imaginary wavelets of the dual-tree complex wavelet transform to simulate the interaction in the attention kernel. In order to further reduce the computational burden of operators, we introduce an adaptive local block shared attention mechanism in the channel domain for our wavelet informed operators. Further, we construct the deep multi-head operator network consisting of a hybrid stack of complex wavelet informed transformer operators and self-attention layers. This enables the Transformer to more sparsely capture multi-scale and multi-directional structured features in the process of learning dependencies. Extensive experimental results show that our adaptive complex wavelet informed transformer operator under the Transformer architecture achieves highly competitive accuracy performance on multiple image classification benchmark datasets. And the proposed operators can be flexibly and effectively migrated to vision tasks in dynamic video scenarios. Codes are available at https://***/Dawn5786/ACWI-Former. © 1999-2012 IEEE.

关键词： Frequency domain analysis

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Uncertainty-aware Semi-supervised Learning Segmentation for Remote Sensing images

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IEEE Transactions on Multimedia 2025年

作者： Lu, Xiaoqiang Li, Lingling Jiao, Licheng Liu, Xu Liu, Fang Ma, Wenping Yang, Shuyuan Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education International Research Center for Intelligent Perception and Computation School of Artificial Intelligence Shaanxi Province Xi'an710071 China

Deep learning based remote sensing (RS) image segmentation significantly impacts several real application scenarios. Behind its success, massive labeled data plays an important role. However, annotating high-resolution RS images requires time-consuming and relevant expertise efforts. To address it, many works dive into semi-supervised learning which utilizes raw information embedded in unlabeled data to improve the segmentation model. Nevertheless, previous studies ignore the integrity and effectiveness of the potential context information hidden in RS data. In this work, we propose an uncertainty-aware masked consistency learning (U-MCL) framework that contains an uncertainty-aware masked denoising (U-MD) module and an uncertainty-aware masked image consistency (U-MIC) module. U-MCL initially generates a patch-wise uncertainty map for each unlabeled image during each training iteration, which is then used to derive an adaptive mask ratio for pseudo-label denoising in U-MD. Simultaneously, the uncertainty map is adopted to model a masked unlabeled image for reasoning unseen areas in U-MIC. Consequently, U-MCL is capable of enhancing model performance by engaging in accurate and stable consistency learning while preserving the integrity of the context and employing the context to infer the predictions of the masked regions safely. Extensive experiments on six RS datasets, i.e., ISPRS Vaihingen, FloodNet, MiniFrance, LoveDA, MER, and MSL, demonstrate the superiority of our U-MCL over recent most advanced methods, achieving new state-of-the-art performance under all benchmarks. Code will be available at https://***/xiaoqiang-lu/U-MCL. © 2025 IEEE.

关键词： Self-supervised learning

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Knowledge-Aware Evolutionary Transformer

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IEEE Transactions on Evolutionary Computation 2025年

作者： Zhang, Ruohan Li, Lingling Jiao, Licheng Liu, Fang Liu, Xu Yang, Shuyuan Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Shaanxi Province Xi'an710071 China

With the Transformer architecture achieving impressive results in the vision domain. It has become a current popular research to explore more potentials of Transformer mixed architectures and explore more suitable mixed combinations. In this paper, Transformer classification network is designed and explored by multi-task architecture search algorithm. A new paradigm for multi-task architecture is designed by combining convolution and Transformer. The designed search architecture can combine the respective advantages of convolution and Transformer and can obtain better performance. At the same time, corresponding knowledge-aware multi-task genetic operators are designed to generate offspring individuals. Inter-task and inter-experience knowledge-aware is utilised to facilitate evolutionary convergence. During the search process, the reference evaluation method is utilised to reduce the redundant computation and time during the search process. In the experimental section, the search results are compared with state-of-the-art architectures and search algorithms. The experimental results confirm the effectiveness and high generalisation of the searched architectures. The ablation experimental part proves the effectiveness of the proposed architectural paradigm, genetic operators and reference evaluation. © 2025 IEEE.

关键词： Convolution

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Cascaded Sliding-Window-Based Relativistic GAN Fusion for Perceptual and Consistent Video Super-Resolution 6th

Cascaded Sliding-Window-Based Relativistic GAN Fusion for P...

引用

6th IFIP TC 12 International Conference on Intelligence Science, ICIS 2024

作者： Li, Dingyi PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9783031712524

Perceptual video super-resolution aims at converting low-resolution videos to visually appealing high-resolution ones. It may lead to temporal inconsistency due to the drastically changing outputs. In this paper, we propose cascaded sliding-window-based relativistic GAN (Generative Adversarial Network) fusion for perceptual and consistent video super-resolution (PC-VSR). Firstly, cascaded sliding-window-based relativistic GAN is designed to extract more useful information. It enlarges the temporal receptive field of sliding-window-based model in each step. It is able to enhance perceptual quality and compensate temporal consistency progressively and sufficiently. The trained separate refinement generator networks are fused into a final refinement generator. The final refinement generator can be calculated recursively at the testing stage. With our generator fusion, the parameter number is reduced and good quality is maintained. Extensive experimental results demonstrate that our approach outperforms state-of-the-art super-resolution methods in terms of perceptual quality. Our method also achieves good temporal consistency and per-pixel accuracy, compared with other perceptual approaches. © IFIP International Federation for Information Processing 2025.

关键词： Generative adversarial networks

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Spatio-Temporal Progressive Attention Model for EEG Classification in Rapid Serial Visual Presentation Task

arXiv

引用

arXiv 2025年

作者： Li, Yang Liu, Wei Feng, Tianzhi Li, Fu Wu, Chennan Fu, Boxun Zhao, Zhifu Wang, Xiaotian Shi, Guangming Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education The School of Artificial Intelligence Xidian University Xi’an710071 China

As a type of multi-dimensional sequential data, the spatial and temporal dependencies of electroencephalogram (EEG) signals should be further investigated. Thus, in this paper, we propose a novel spatial-temporal progressive attention model (STPAM) to improve EEG classification in rapid serial visual presentation (RSVP) tasks. STPAM first adopts three distinct spatial experts to learn the spatial topological information of brain regions progressively, which is used to minimize the interference of irrelevant brain regions. Concretely, the former expert filters out EEG electrodes in the relative brain regions to be used as prior knowledge for the next expert, ensuring that the subsequent experts gradually focus their attention on information from significant EEG electrodes. This process strengthens the effect of the important brain regions. Then, based on the above-obtained feature sequence with spatial information, three temporal experts are adopted to capture the temporal dependence by progressively assigning attention to the crucial EEG slices. Except for the above EEG classification method, in this paper, we build a novel Infrared RSVP EEG Dataset (IRED) which is based on dim infrared images with small targets for the first time, and conduct extensive experiments on it. The results show that our STPAM can achieve better performance than all the compared methods. © 2025, CC BY.

关键词： Electroencephalography

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