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arXiv 2022年

作者： Shi, Guanghui Mao, Shasha Gou, Shuiping Yan, Dandan Jiao, Licheng Xiong, Lin Key Lab of Intelligent Perception and Image Understanding Ministry of Education Xidian University Xi’an China Xidian University Xian China School of Electronic Engineering Xidian University China Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education of China China

Facial expression recognition (FER) is still one challenging research due to the small inter-class discrepancy in the facial expression data. In view of the significance of facial crucial regions for FER, many existing researches utilize the prior information from some annotated crucial points to improve the performance of FER. However, it is complicated and time-consuming to manually annotate facial crucial points, especially for vast wild expression images. Based on this, a local non-local joint network is proposed to adaptively light up the facial crucial regions in feature learning of FER in this paper. In the proposed method, two parts are constructed based on facial local and non-local information respectively, where an ensemble of multiple local networks are proposed to extract local features corresponding to multiple facial local regions and a non-local attention network is addressed to explore the significance of each local region. Especially, the attention weights obtained by the non-local network is fed into the local part to achieve the interactive feedback between the facial global and local information. Interestingly, the non-local weights corresponding to local regions are gradually updated and higher weights are given to more crucial regions. Moreover, U-Net is employed to extract the integrated features of deep semantic information and low hierarchical detail information of expression images. Finally, experimental results illustrate that the proposed method achieves more competitive performance compared with several state-of-the-art methods on five benchmark datasets. Noticeably, the analyses of the non-local weights corresponding to local regions demonstrate that the proposed method can automatically enhance some crucial regions in the process of feature learning without any facial landmark information. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Budgeted Influence Maximization via Boost Simulated Annealing in Social Networks

arXiv

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arXiv 2022年

作者： Wu, Jianshe Gao, Junjun Zhu, Hongde Zhang, Zulei The Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education International Research Center for Intelligent Perception and Computation Xidian University Xian710071 China

Due to much closer to real application scenarios, the budgeted influence maximization (BIM) problem has attracted great attention among researchers. As a variant of the influence maximization (IM) problem, the BIM problem aims at mining several nodes with different costs as seeds with limited budget to maximize the influence as possible. By first activating these seed nodes and spreading influence under the given propagation model, the maximized spread of influence can be reached in the network. Several approaches have been proposed for BIM. Most of them are modified versions of the greedy algorithm, which work well on the IM but seems inefficient for the BIM because huge time consuming is inevitable. Recently, some intelligence algorithms are proposed in order to reduce the running time, but analysis shows that they cannot fully utilize the relationships between nodes in networks, which will result in influence loss. Inspired by this, we propose an efficient method based on boosted simulated annealing (SA) algorithm in this paper. Three heuristic strategies are proposed to improve the performance and speed up the proposed algorithm. Experimental results on both real world and synthetic networks demonstrate that the proposed boosted SA performs much better than existed algorithms on performance with almost equal or less running time. © 2022, CC BY.

关键词： Simulated annealing

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Enhancing Cross-Dataset EEG Emotion Recognition: A Novel Approach with Emotional EEG Style Transfer Network

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IEEE Transactions on Affective Computing 2025年

作者： Zhou, Yijin Li, Fu Li, Yang Ji, Youshuo Zhang, Lijian Chen, Yuanfang Wang, Huaning Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xi'an710071 China Beijing Institute of Mechanical Equipment Beijing100000 China Fourth Military Medical University Department of Psychiatry at Xijing Hospital Xi'an China

Electroencephalogram (EEG)-based emotion recognition has achieved remarkable success in both subject-dependent and subject-independent scenarios. However, overcoming the challenges associated with reduced performance in EEG emotion recognition across devices, time, space, and subjects (i.e., cross-dataset) remains a significant obstacle for affective brain-computer interfaces (aBCIs). The key issue lies in the distributional mismatch between source and target domain EEG signals. To tackle the significant inter-domain differences in cross-dataset EEG emotion recognition, this paper introduces an innovative framework termed the Emotional EEG Style Transfer Network (E2STN), which aims to effectively capture the emotional content information from the source domain and the style features from the target domain, facilitating the reconstruction of stylized emotion EEG representations. These stylized EEG representations significantly enhance the discriminative prediction performance in cross-dataset EEG emotion recognition. Specifically, E2STN consists of three key modules: a Transfer Module for domain style transfer, a Transfer Evaluation Module for evaluating transfer quality, and a Discriminative Module for making discriminative predictions. Extensive experiments demonstrate that EE2STN achieves state-of-the-art performance in cross-dataset emotion EEG recognition. To the best of our knowledge, this is the first work to explicitly address cross-dataset emotion EEG recognition. The experimental results provide a valuable benchmark for future research in this area. The associated code is publicly available at https://***/zhouyijin1998/EESTN. © 2010-2012 IEEE.

关键词： Electroencephalography

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Task Context Transformer and Gcn for Few-Shot Learning of Cross-Domain

SSRN

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SSRN 2023年

作者： Li, Pengfang Liu, Fang Jiao, Licheng Li, Lingling Chen, Puhua Li, Shuo Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education China International Research Center for Intelligent Perception and Computation China Joint International Research Laboratory of Intelligent Perception and Computation China School of Artificial Intelligent Xidian University No.2 Taibai South Road Shaanxi Xi’an710071 China

Cross-Domain Few-Shot Learning (CDFSL) to recognize new categories in a new domain with few samples has attracted significant attention. Recently, task-specific CDFSL emerges as promising research for its great generalization. However, the existing task-specific CDFSL methods are not robust enough in task context modeling and task-specific feature learning, especially under the cross-domain setting. To tackle this problem, a Task Context Transformer and Graph Convolutional Network (TCT-GCN) method for CDFSL is proposed. The proposed TCT-GCN constructs three modules: 1) Multi-Level Feature Fusion, which fuses domain-shared low-level features into domain-unshared high-level features;2) Transformer-based Task Context Encoder, which models robust task context;3) Graph Convolutional Network-based Adaptive Feature Learner, which adaptively learns task-specific features. Experiments on four CDFSL datasets reveal the effectiveness of the proposed TCT-GCN. © 2023, The Authors. All rights reserved.

关键词： Convolution

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A Single-Loop Accelerated Extra-Gradient Difference Algorithm with Improved Complexity Bounds for Constrained Minimax Optimization 37

A Single-Loop Accelerated Extra-Gradient Difference Algorith...

引用

37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Liu, Yuanyuan Shang, Fanhua An, Weixin Liu, Junhao Liu, Hongying Lin, Zhouchen Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University China School of Computer Science and Technology College of Intelligence and Computing Tianjin University China Medical College Tianjin University China National Key Lab of General AI School of Intelligence Science and Technology Peking University China Institute for Artificial Intelligence Peking University China Peng Cheng Laboratory China

ISBN: (纸本)9781713899921

In this paper, we propose a novel extra-gradient difference acceleration algorithm for solving constrained nonconvex-nonconcave (NC-NC) minimax problems. In particular, we design a new extra-gradient difference step to obtain an important quasi-cocoercivity property, which plays a key role to significantly improve the convergence rate in the constrained NC-NC setting without additional structural assumption. Then momentum acceleration is also introduced into our dual accelerating update step. Moreover, we prove that, to find an Ε-stationary point of the function f, the proposed algorithm attains the complexity O(Ε-2) in the constrained NC-NC setting, while the best-known complexity bound is Oe(Ε-4), where Oe(·) hides logarithmic factors compared to O(·). As the special cases of the constrained NC-NC setting, the proposed algorithm can also obtain the same complexity O(Ε-2) for both the nonconvex-concave (NC-C) and convex-nonconcave (C-NC) cases, while the best-known complexity bounds are Oe(Ε-2.5) for the NC-C case and Oe(Ε-4) for the C-NC case. For fair comparison with existing algorithms, we also analyze the complexity bound to find Ε-stationary point of the primal function φ for the constrained NC-C problem, which shows that our algorithm can improve the complexity bound from Oe(Ε-3) to O(Ε-2). To the best of our knowledge, this is the first time that the proposed algorithm improves the best-known complexity bounds from Oe(Ε-4) and Oe(Ε-3) to O(Ε-2) in both the NC-NC and NC-C settings. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Constrained optimization

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Fast Node Selection of Networked Radar Based on Transfer Reinforcement Learning 5th

Fast Node Selection of Networked Radar Based on Transfer R...

引用

5th IFIP TC 12 International Conference on Intelligence Science, ICIS 2022

作者： Cao, Yanjun Wang, Yuan Guo, Jingjing Han, Li Zhang, Chao Zhu, Jin Zhang, Tianyang Zhang, Xiangrong The 54th Research Institute of China Electronics Technology Group Corporation Beijing China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Shaanxi Xian710071 China National Laboratory of Radar Signal Processing Xidian University Xian710071 China

ISBN: (纸本)9783031149023

The networked radar system can synthesize different echo signals received by various radars and realize the cooperative detection of multiple radars, becoming more and more critical for data fusion sharing and network collaboration. However, due to the large number and wide range of nodes in the networked radar system, there exists a redundancy problem in radar node assignment, which causes additional resource consumption and slows down the task execution speed of radar node selection. To solve the above problem, this paper proposes a fast radar node selection method based on transfer reinforcement learning to quickly select the optimal and minimum node resources. The proposed method devises a novel reward function for the Monte Carlo Tree and a different termination state of iteration to select the minimize the number of radar nodes. In order to further accelerate the selection of radar nodes, transfer reinforcement learning is presented to fully leverage the previous knowledge. Experimental results show that our proposed method can quickly select the optimal and minimum radar nodes in a brief period, significantly improving the speed of radar node selection in the networked radar. © 2022, IFIP International Federation for Information Processing.

关键词： Reinforcement learning

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Robust Motion-Guided Frame Sampler with Interpretive Evaluation for Video Action Recognition

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IEEE Transactions on Mobile Computing 2025年

作者： Bai, Jing Zhang, Yuxiang Wang, Yiran Xiao, Zhu Xiong, Yong Jiao, Licheng Xidian University School of Artificial Intelligence Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education Xi'an710071 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Hunan Lianzhi Technology Co. Ltd Changsha410200 China

Due to the presence of redundancy and interference, frame sampling is a promising but challenging solution to mitigate the expensive computation of video action recognition. Although the motion prior has shown great potential for frame selection, existing motion-based strategies suffer from limitations in terms of robustness and interpretive evaluation. In this paper, we devise a robust frame sampling strategy called positive motion guided sampler (PMGSampler). It consists of two procedures, local motion capture and global motion statistics. At the local level, we propose two concepts about inter-frame motion amplitude and motion continuity, which helps to perceive the movement of subjects and identify abnormal events that may generate negative pseudo-motion information. Then, through a global analysis of the obtained local motions, the sampler becomes more sensitive to informative frames and robust to outliers. The proposed sampler can be applied to most existing models for improving recognition accuracy. We conduct extensive experiments on four widely-used benchmarks to demonstrate the superiority of our PMGSampler over other methods of the same type. In addition, to analyse how sampled frames influence action recognition, we present a visual interpretation method for video models, termed as spatio-temporal class activation map (STCAM). By introducing spatial and temporal branches, our STCAM is able to visualise the salience of spatio-temporal features. With the help of STCAM, we can further intuitively evaluate the performance of different sampling strategies. Code is available at https://***/zyx-cv/PMGSampler. © 2002-2012 IEEE.

关键词： Motion capture

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Self-Constrained Graph Stochastic Neural Networks for Graphstructure Learning

SSRN

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SSRN 2022年

作者： Jia, Nan Tian, Xiaolin Wang, Kai Huang, Xiaocui Key Lab of Intelligent Perception and Image Understanding of Ministry of Education International Center of Intelligent Perception and Computation Xidian University Xi’an710071 China

Graph Neural Networks (GNNs) have emerged as a powerful and flexible framework for graph structure ***, most existing GNNs heavily rely on graph structure and may suffer from the limitation of over-smoothingwhen we enlarge the feature propagation steps. In this paper, a simple yet effective framework—the Self-ConstrainedGraph Stochastic Neural Networks (SCGSNN) is proposed to address these two issues. Firstly, we use the multibranchgenerating structure to learn the adjacency matrix and the GNN parameters simultaneously in SCGSNN,aiming to obtain multiple different first-order adjacency matrices. Then, we develop the self-constrained mechanismto constrain each generation structure branch to infer a more realistic graph structure. Finally, we apply the mixedmulti-level information aggregation to alleviate the issue of over-smoothing. Experimental results show that SCGSNNcan be extended to large graphs with million-level nodes and apparently outperforms state-of-the-art GNNs baselinesfor the node classification task. © 2022, The Authors. All rights reserved.

关键词： Graphic methods

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Hyper-parameter Tuning of Federated Learning Based on Particle Swarm Optimization 7

Hyper-parameter Tuning of Federated Learning Based on Partic...

引用

7th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2021

作者： Li, Zhiyuan Li, Hao Zhang, Mingyang Xidian University School of Electronic Engineering Key Laboratory of lntelligent Perception and Image Understanding of Ministry of Education of China Xi'an710071 China

ISBN: (纸本)9781665441490

The learning task of federated learning (FL) is solved by a federation of a center server and individual clients. Contrary to traditional deep learning models, federated learning consists of two parts, the global model and individual models. However, since the process is carried out by two parts of equal importance, it is ideal to deal with both parts simultaneously. To achieve superior performance, federated learning requires carefully selected hyper-parameters, which has more hyper-parameters than those of traditional deep learning models. To solve this tuning problem, we propose a method using particle swarm optimization (PSO) algorithm to tune the hyperparameters of federated learning. PSO algorithm is a gradient-free, stochastic optimization method which is better than grid search method when it comes to large search space. It helps locate the optimal combination of multiple parameters. In this article, we focus on applying PSO method on tuning the hyper-parameters of FL models, and prove that it is an efficient way to acquire satisfactory results. Experiments on MNIST dataset with convolution neural networks have proved the superiority of the proposed method. © 2021 IEEE.

关键词： Deep learning

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Integrating Language-image Prior into EEG Decoding for Cross-Task Zero-Calibration RSVP-BCI

arXiv

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arXiv 2025年

作者： Li, Xujin Wei, Wei Qiu, Shuang Zhang, Xinyi Li, Fu He, Huiguang The Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology Institute of Automation Chinese Academy of Sciences Beijing100190 China Beijing100049 China The Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education The School of Artificial Intelligence Xidian University Xi’an710071 China

Rapid Serial Visual Presentation (RSVP)-based Brain-Computer Interface (BCI) is an effective technology used for information detection by detecting Event-Related Potentials (ERPs). The current RSVP decoding methods can perform well in decoding EEG signals within a single RSVP task, but their decoding performance significantly decreases when directly applied to different RSVP tasks without calibration data from the new tasks. This limits the rapid and efficient deployment of RSVP-BCI systems for detecting different categories of targets in various scenarios. To overcome this limitation, this study aims to enhance the cross-task zero-calibration RSVP decoding performance. First, we design three distinct RSVP tasks for target image retrieval and build an open-source dataset containing EEG signals and corresponding stimulus images. Then we propose an EEG with Language-image Prior fusion Transformer (ELIPformer) for cross-task zero-calibration RSVP decoding. Specifically, we propose a prompt encoder based on the language-image pre-trained model to extract language-image features from task-specific prompts and stimulus images as prior knowledge for enhancing EEG decoding. A cross bidirectional attention mechanism is also adopted to facilitate the effective feature fusion and alignment between the EEG and language-image features. Extensive experiments demonstrate that the proposed model achieves superior performance in cross-task zero-calibration RSVP decoding, which promotes the RSVP-BCI system from research to practical application. Copyright © 2025, The Authors. All rights reserved.

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