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IEEE Transactions on Artificial Intelligence 2023年第1期4卷 23-43页

作者： Jiao, Licheng Gao, Jie Liu, Xu Liu, Fang Yang, Shuyuan Hou, Biao Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University Xi'an710071 China

Feature representation has been widely used and developed recently. Multiscale features have led to remarkable breakthroughs for representation learning process in many computer vision tasks. This paper aims to provide a comprehensive survey of the recent multiscale representation learning achievements in classification tasks. Multiscale representation learning methods can be divided into two broad categories (multiscale geometric analysis and multiscale networks). Eleven kinds of multiscale geometric tools and seven kinds of multiscale networks are introduced. Some corresponding fundamental subproblems of these two broad categories are also described, including some concepts in representation process, specific representation methods with multiscale geometric analysis, and multiscale representation design strategies for networks. Then, the correlation between these two broad categories is illustrated, including respective characteristics, combination strategies, and characteristics of optimal representation. Some datasets and evaluation results are included to verify the effectiveness of the multiscale representation learning. Eventually, conclusion and future work are given, covering four directions [a) Choice and fusion;b) Self-adaption;c) Structure;and d) Generalization and proof]. © 2022 IEEE.

关键词： Deep neural networks

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VLPA-CLIP: Video Language Prompting and Adapting CLIP for efficient video action recognition

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Pattern Recognition 2025年 168卷

作者： Hao Wang Fang Liu Licheng Jiao Jiahao Wang Shuo Li Lingling Li Puhua Chen Xu Liu Wenping Ma 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 Xi’an 710071 Shaanxi China

Adapting the Contrastive Language-image Pre-training (CLIP) model toward video action recognition has recently shown remarkable performance on multiple benchmarks. However, recent work lacks trade-offs between fully supervised and zero-shot, few-shot performance. Full fine-tuning compromises zero-shot generalization while keeping the backbone frozen reduces supervised performance. This work proposes a parameter-efficient fine-tuning scheme to adapt pre-trained image models for video action recognition while maintaining a balance between fully supervised and zero-shot performance. Our approach on the vision side includes the following three aspects: (1) Domain Transfer Module to model the video data distribution, (2) Motion Enhancement Module to provide video-specific temporal cues, and (3) key–value prompts in self-attention to guide the attention computation during fine-tuning with no additional inference latency. Moreover, we define a hierarchical prompting scheme on the text side to achieve text context learning and a multi-modal attention module to reduce redundant features and learn more discriminative video text representations. With this approach, we can demonstrate exceptional fully supervised performance on Kinetics-400 and SSv2 while remaining competitive in zero-shot settings on HMDB-51 and UCF-101. We optimize a smaller number of parameters and preserve the existing unified vision language representation space while keeping the pretrained backbone frozen, bolstering zero-shot performance.

关键词： Unified Modeling Language

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Immunodominance and clonal selection inspired multiobjective clustering

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Progress in Natural Science:Materials International 2009年第6期19卷 751-758页

作者： Wenping Ma , Licheng Jiao, Maoguo Gong key laboratory of intelligent perception and image understanding of the ministry of education of China, Institute of intelligent Information Processing, Xidian University, Xi’an 710071, China [a]Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi’an 710071 China

The biological immune system is a highly parallel and distributed adaptive system. The information processing abilities of the immune system provide important insights into the field of computation. Based on immunodominance in the biological immune system and the clonal selection mechanism, a novel data mining method, Immune Dominance Clonal Multiobjective Clustering algorithm (IDCMC), is presented. The algorithm divides an individual population into three sub-populations according to three different measurements, and adopts different evolution and selection strategies for each sub-population. The update of each sub-population, however, is not carried out in isolation. The periodic combination operation of the analysis of the three sub-populations represents considerable advantages in its global search ability. The clustering task is a multiobjective optimization problem, which is more robust with respect to the variety of cluster structures of different datasets than a single-objective clustering algorithm. In addition, the new algorithm can determine the number of clusters automatically, which should identify the most promising clustering solutions in the candidate set. The experimental results, using artificial datasets with different manifold structure and handwritten digit datasets, show that the IDCMC outperforms the PESA-Ⅱ-based clustering method, the genetic algorithm-based clustering technique and the original K-Means algorithm in solving most of the problems tested.

关键词： Artificial immune systems Multiobjective optimization Clustering Unsupervised learning

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A novel quantum-inspired immune clonal algorithm with the evolutionary game approach

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Progress in Natural Science:Materials International 2009年第10期19卷 1341-1347页

作者： Qiuyi Wu,Licheng Jiao,Yangyang Li,Xiaozheng Deng key laboratory of intelligent perception and image understanding of ministry of education of China,Institute of intelligent Information Processing,Xidian University,Xi’an 710071,China [a]Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi’an 710071 China

The quantum-inspired immune clonal algorithm(QICA) is a rising intelligence *** on evolutionary game theory and QICA,a quantum-inspired immune algorithm embedded with evolutionary game(EGQICA) is proposed to solve combination optimization *** this paper,we map the quantum antibody’s finding the optimal solution to player’s pursuing maximum utility by choosing strategies in evolutionary *** dynamics is used to model the behavior of the quantum antibody and the memory mechanism is also introduced in this *** results indicate that the proposed approach maintains a good diversity and achieves superior performance.

关键词： Game theory Evolutionary game Replicator dynamics Quantum-inspired optimization Artificial immune system

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Visible-infrared person re-identification with region-based augmentation and cross modality attention

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Scientific reports 2025年第1期15卷 18225页

作者： Yuwei Guo Wenhao Zhang Licheng Jiao Shuang Wang Shuo Wang Fang Liu Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education of China School of Artificial Intelligence International Research Center of Intelligent Perception and Computation Xidian University Xi'an 710071 China. yuweiguo18@***. Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education of China School of Artificial Intelligence International Research Center of Intelligent Perception and Computation Xidian University Xi'an 710071 China. School of Computer Science The University of Birmingham Birmingham B15 2TT UK.

Visible-infrared person re-identification (VI-ReID) aims to search the same pedestrian of interest across visible and infrared modalities. Existing models mainly focus on compensating for modality-specific information to reduce modality variation. However, these methods often introduce interfering information and lead to higher computational overhead when generating the corresponding images or features. Additionally, the pedestrian region characteristics in VI-ReID are not effectively utilized, thus resulting in ambiguous or unnatural images. To address these issues, it is critical to leverage pedestrian attentive features and learn modality-complete and -consistent representation. In this paper, a novel Region-based Augmentation and Cross Modality Attention (RACA) model is proposed, focusing on the pedestrian regions to efficiently compensate for missing modality-specific features. Specifically, we propose a region-based data augmentation module PedMix to enhance pedestrian region coherence by mixing the corresponding regions from different modalities, thus generating more natural images. Moreover, a lightweight hybrid compensation module, i.e., a Modality Feature Transfer (MFT) module, is proposed to integrate cross attention and convolution networks to avoid introducing interfering information while preserving minimal computational overhead. Extensive experiments conducted on the benchmark SYSU-MM01 and RegDB datasets demonstrated the effectiveness of our proposed RACA model.

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Air Pollution Monitoring By Integrating Local and Global Information in Self-Adaptive Multiscale Transform Domain

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IEEE Transactions on Multimedia 2025年

作者： Gu, Ke Liu, Yuchen Liu, Hongyan Liu, Bo Qiao, Junfei Lin, Weisi Zhang, Wenjun Beijing University of Technology School of Information Science and Technology Engineering Research Center of Intelligent Perception and Autonomous Control of Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China Massey University School of Mathematical and Computational Sciences New Zealand Nanyang Technological University College of Computing and Data Science Singapore Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai China

As the deadliest form of pollution, air pollution had a prolonged severe damage to the human health and life safety of nearly 99% of the world's population. Facing to the problem that billions of tons of pollutants discharged into the air per year by industrial activities, this paper proposed a novel image-based air pollution monitor (IAPM) by incorporating local and global information in the self-adaptive multiscale transform domain, so as to achieve the timely and effective leakage detection of typical air pollutants from a single image. To be specific, this paper first developed a screen-shaped module according to two significant findings in visual neuroscience, which include the high sensitivity of human eyes to horizontal and vertical stimuli and the center-surround inhibition, by designing and fusing the square module, horizontal strip module and vertical strip module parallelly for simulating the behaviour of human eyes to extract local features. Second, the learnable weights and proportional mapping were applied to incorporate the screen-shaped module and lightweight vision transformer as backbone, towards more richly exploiting and fusing local and global information just as the way a brain perceives external stimuli. Third, a new self-adaptive multiscale transform domain method was devised based on two motivations from the visual characteristics of multiscale perception and the brain characteristics of self-adaptive domain transform to modify the backbone by using the operations of pooling and pointwise convolution. Extensive experiments implemented on the datasets of carbon particulate matters and ethylene leakage confirmed the superior monitoring performance of the proposed IAPM model beyond the state-of-the-art (SOTA) peers by an accuracy gain of about 4%. Furthermore, the proposed IAPM model only required 0.089 GFLOPs and 0.15 million model parameters, remarkably outperforming SOTA competitors in computational efficiency and storage resources. © 1999-2

关键词： Information leakage

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Generalization-aware Remote Sensing Change Detection via Domain-agnostic Learning

arXiv

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arXiv 2025年

作者： Zang, Qi Wang, Shuang Zhao, Dong Quan, Dou Hu, Yang Jiao, Licheng Yang, Zhi Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University Xi’an710071 China DFH Satellite Co. Ltd CAST Beijing100080 China

Change detection has essential significance for the region’s development, in which pseudo-changes between bitemporal images induced by imaging environmental factors are key challenges. Existing transformation-based methods regard pseudo-changes as a kind of style shift and alleviate it by transforming bitemporal images into the same style using generative adversarial networks (GANs). However, their efforts are limited by two drawbacks: 1) Transformed images suffer from distortion that reduces feature discrimination. 2) Alignment hampers the model from learning domain-agnostic representations that degrades performance on scenes with domain shifts from the training data. Therefore, oriented from pseudo-changes caused by style differences, we present a generalizable domain-agnostic difference learning network (DonaNet). For the drawback 1), we argue for local-level statistics as style proxies to assist against domain shifts. For the drawback 2), DonaNet learns domain-agnostic representations by removing domain-specific style of encoded features and highlighting the class characteristics of objects. In the removal, we propose a domain difference removal module to reduce feature variance while preserving discriminative properties and propose its enhanced version to provide possibilities for eliminating more style by decorrelating the correlation between features. In the highlighting, we propose a cross-temporal generalization learning strategy to imitate latent domain shifts, thus enabling the model to extract feature representations more robust to shifts actively. Extensive experiments conducted on three public datasets demonstrate that DonaNet outperforms existing state-of-the-art methods with a smaller model size and is more robust to domain shift. © 2025, CC BY.

关键词： Change detection

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Multiple Resolutions Detail Enhancement Network for Real-Time image Semantic Segmentation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3393-3407页

作者： Gu, Jing Sun, Xinkai Feng, Jie Yang, Shuyuan Liu, Fang Jiao, Licheng Xidian University Key Lab of Intelligent Perception and Image Understanding of Ministry of Education Xi'an710071 China

Real-time image semantic segmentation (ISS) draws the attentions of more and more researchers as a basis of scene understanding, and it has been applied in many fields that need fast interaction and response, such as autonomous driving and robot control. Considering the loss of low-level spatial information with the deepening network layer, we propose a multiple resolutions detail enhancement network (MRDENet) in this article, which adequately extracts and utilizes accurate low-level detail information from original images with different resolutions. MRDENet consists of three light-weight branch subnetworks, and designs dense oblique connections between adjacent branches to preserve the different level effective features of previous branch. Furthermore, a new multilevel information aggregation (MIA) module is presented to effectively fuse the low-level detail features and the high-level semantic features of different branches by employing group convolution (group conv) and channel shuffle with low computation cost, thus ensuring that MRDENet could achieve a favorable tradeoff between segmentation precision with inference speed. The experimental results show that MRDENet achieves 73.1% mean intersection over union (mIoU) with 93 frames per second (FPS) on Cityscapes dataset, and 68.5% mIoU with 112 FPS on CamVid dataset, which indicates the performance of MRDENet is competitive with the state-of-art methods. © 2020 IEEE.

关键词： Real time systems

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GAUSSIAN PRINCIPLE COMPONENTS FOR NONLOCAL MEANS image DENOISING

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Journal of Electronics(China) 2011年第4期28卷 539-547页

作者： Li Xiangping Wang Xiaotian Shi Guangming Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education of China Xidian University Xi＇ an 710071 China

NonLocal Means(NLM),taking fully advantage of image redundancy,has been proved to be very effective in noise ***,high computational load limits its wide *** on Principle Component Analysis(PCA),Principle Neighborhood Dictionary(PND) was proposed to reduce the computational load of ***,as the principle components in PND method are computed directly from noisy image neighborhoods,they are prone to be inaccurate due to the presence of *** this paper,an improved scheme for image denoising is *** scheme is based on PND and uses preprocessing via Gaussian filter to eliminate the influence of *** is then used to project those filtered image neighborhood vectors onto a lower-dimensional *** the preproc-essing process,the principle components computed are more accurate resulting in an improved de-noising performance.A comparison with some NLM based and state-of-art denoising methods shows that the proposed method performs well in terms of Peak Signal to Noise Ratio(PSNR) as well as image visual *** experimental results demonstrate that our method outperforms existing methods both subjectively and objectively.

关键词： image denoising NonLocal Means(NLM) Gaussian filter Principle Component Analysis(PCA)

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Texture and wavelet based PCANet (TW-PCANet) for infants' facial expression recognition 2019

Texture and wavelet based PCANet (TW-PCANet) for infants' fa...

引用

2019 International Conference on Artificial Intelligence and Computer Science, AICS 2019

作者： Shen, Fang Liu, Jing Wu, Peng Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an China

ISBN: (纸本)9781450371506

The babyhood is a very important stage in the human growth process. Thus learning the facial expression characteristics of infants is of great significance to nursing care for infants. Infants' faces are significantly different from adults' faces, like eyebrows, eyes, nose, cheeks, skin texture, etc. Therefore, the facial expression recognition models trained on adults' facial expression database cannot be directly applied to infants' facial expression recognition. There are dozens of facial expression databases for adults' facial expression analysis, but there is no database composed by various infants' facial expressions. Hence we first collect candid images of infants' facial expression, which includes six obvious expressions of infants, namely crying, curious, happy, sleeping, wronged, neutral. Then the texture and wavelet based PCANet (TW-PCANet) is proposed to fully extract the features of infants' facial expressions. The proposed method first handles original input color images by local binary patterns (LBP) and Gabor wavelet, then the processed images are put into the PCANet to get the final class. The experimental results show that the proposed method can achieve higher recognition rate obtained by PCANet, LBP and Gabor wavelet. © 2019 Association for Computing Machinery.

关键词： Local binary pattern

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