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Prototype-Guided Class-Balanced Active Domain Adaptation for Hyperspectral image Classification

IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Luo, Haiyang Zhong, Shengwei Gong, Chen Nanjing University of Science and Technology PCA Laboratory the Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Laboratory of Image and Video Understanding for Social Security the School of Computer Science and Engineering Nanjing 210094 China

The high cost of data annotation has become a major factor restricting the hyperspectral image (HSI) classification task. To address this issue, domain adaptation (DA) techniques have been developed to adapt models trained on abundantly labeled HSIs to those with scarce labels. As a novel DA paradigm, active domain adaptation (ADA) seeks to selectively annotate informative examples using active learning (AL) techniques under domain shift scenarios, ultimately enhancing model adaptation performance. However, current ADA methods require annotating a relatively large number of target examples, which is impractical for HSIs. Additionally, the target HSIs suffer from class imbalance, which limits the adaptation performance. To address the above issues, this paper proposes a prototype-guided class-balanced active domain adaptation (PCADA) method for HSI classification. PCADA alternately aligns the distributions between domains through prototype guidance and selects the most valuable target examples for annotation. Specifically, a prototype-guided domain alignment (PGDA) module is introduced, which generates target prototypes based on highly confident pseudo-labels and aligns the distributions of two domains. The inconsistency-aware example selection (IES) module identifies target-specific examples and select the most valuable ones for annotation. Furthermore, we propose a class-balanced self-training (CBST) module that generates pseudo-labels with balanced class distribution to solve the class imbalance issue in target domain. The experimental results conducted on multiple benchmark HSI datasets demonstrate the superior performance of our proposed method. © 1980-2012 IEEE.

关键词： active domain adaptation class imbalance Hyperspectral image classification

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Adaptive multifactorial particle swarm optimisation

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CAAI Transactions on Intelligence Technology 2019年第1期4卷 37-46页

作者： Zedong Tang Maoguo Gong School of Electronic Engineering Key Laboratory of Intelligent Perception and Image Understanding of Ministry of EducationXidian UniversityNo.2 South TaiBai RoadXi’an 710071People’s Republic of China

Existing multifactorial particle swarm optimisation(MFPSO)algorithms only explore a relatively narrow area between the inter-task ***,these algorithms use a fixed inter-task learning probability throughout the evolution ***,the parameter is problem dependent and can be various at different stages of the *** this work,the authors devise an inter-task learning-based information transferring mechanism to replace the corresponding part in *** inter-task learning mechanism transfers the searching step by using a differential term and updates the personal best position by employing an inter-task *** this mean,the particles can explore a broad search space when utilising the additional searching experiences of other *** addition,to enhance the performance on problems with different complementarity,they design a self-adaption strategy to adjust the inter-task learning probability according to the performance *** compared the proposed algorithm with the state-of-the-art algorithms on various benchmark *** results demonstrate that the proposed algorithm can transfer inter-task knowledge efficiently and perform well on the problems with different complementarity.

关键词： MFPSO multifactorial particle swarm optimisation

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Updating Land-Cover Maps by Iterative Difference Learning Network

Updating Land-Cover Maps by Iterative Difference Learning Ne...

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IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)

作者： Mingliang Zhang Zheng Feng Jinxin Wei Maoguo Gong Key Laboratory of lntelligent Perception and Image Understanding of Ministry of Education Xidian University Xi’an China School of Aerospace Science and Technology Xidian University Xi’an China

ISBN: (纸本)9781665441506

Multi-temporal remote sensing image classification aims to exploit the available information of image in the source domain to classify target domain. Since the manual labeling is time-consuming and labor-intensive, it is unrealistic to have enough labels for all images of the time series. By analyzing the difference information of multi-temporal images, the labels of unchanged region can be transferred form source domain to the target domain. In order to further utilize the difference information and learn a robust classifier, we propose an iterative difference learning network (IDLnet) to update land-cover maps in this paper. The proposed method aims at optimizing the process of label transfer by analyzing results of classifier and to fine-tuning it with a series of dynamic training sets. In proposed method, we first utilize the source domain data to initialize a training set and train a classifier to classify both the source and target domains. The change detection (CD) is applied on the ground image datasets and the classification result. Then the transfer learning (TL) is employed to transfer the unchanged information to fine-tuning network. We detect changes of the classification result images again and fuse the previous CD results. Finally, the accuracy cannot be improved after several iterations of fine-tuning the classifier.

关键词： Training Fuses Conferences Transfer learning Time series analysis Manuals Labeling

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Inference guided feature generation for generalized zero-shot learning

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Neurocomputing 2021年 430卷 150-158页

作者： Zongyan Han Zhenyong Fu Guangyu Li Jian Yang 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 Jiangsu 210094 China

Generalized zero-shot learning suffers from an extreme data imbalance problem, that is, the training data only come from seen classes while no unseen class data are available. Recently, a number of feature generation methods based on generative adversarial networks (GAN) have been proposed to address this problem. Existing feature generation methods, however, have never considered the under-constrained problem, and thus could generate an unrestricted visual feature corresponding to no meaningful object class. In this paper, we propose to equip the feature generation framework with a parallel inference network that projects visual feature to the semantic descriptor space, constraining to avoid the generation of unrestricted visual features. The two-parallel-stream framework (1) enables our method, termed inference guided feature generation (Inf-FG), to mitigate the under-constrained problem and (2) makes our Inf-FG applicable to transductive ZSL. Our Inf-FG learns the feature generator and the inference network simultaneously by aligning the joint distribution of visual features and semantic descriptors from the feature generator and the joint distribution from the inference network. We evaluate our approach on four benchmark ZSL datasets, including AWA, CUB, SUN, and FLO, on which our method improves our baselines on generalized zero-shot learning.

关键词： Feature generation Generalized zero-shot learning image classification Transfer learning

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AudioVisual video summarization

arXiv

引用

arXiv 2021年

作者： Zhao, Bin Gong, Maoguo Li, Xuelong The Academy of Advanced Interdisciplinary Research Xidian University Xi’an710071 China Northwestern Polytechnical University Xi’an710072 China The Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education International Research Center for Intelligent Perception and Computation Xidian University Xi’an710071 China

Audio and vision are two main modalities in video data. Multimodal learning, especially for audiovisual learning, has drawn considerable attention recently, which can boost the performance of various computer vision tasks. However, in video summarization, existing approaches just exploit the visual information while neglect the audio information. In this paper, we argue that the audio modality can assist vision modality to better understand the video content and structure, and further benefit the summarization process. Motivated by this, we propose to jointly exploit the audio and visual information for the video summarization task, and develop an AudioVisual Recurrent Network (AVRN) to achieve this. Specifically, the proposed AVRN can be separated into three parts: 1) the two-stream LSTM is utilized to encode the audio and visual feature sequentially by capturing their temporal dependency. 2) the audiovisual fusion LSTM is employed to fuse the two modalities by exploring the latent consistency between them. 3) the self-attention video encoder is adopted to capture the global dependency in the video. Finally, the fused audiovisual information, and the integrated temporal and global dependencies are jointly used to predict the video summary. Practically, the experimental results on the two benchmarks, i.e., SumMe and TVsum, have demonstrated the effectiveness of each part, and the superiority of AVRN compared to those approaches just exploiting visual information for video summarization. Copyright © 2021, The Authors. All rights reserved.

关键词： Video recording

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Bridge the Gap between Supervised and Unsupervised Learning for Fine-Grained Classification

SSRN

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SSRN 2023年

作者： Wang, Jiabao Li, Yang Wei, Xiu-Shen Li, Hang Miao, Zhuang Zhang, Rui Army Engineering University of PLA Jiangsu Nanjing210007 China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing210094 China State Key Lab. for Novel Software Technology Nanjing University Jiangsu Nanjing210023 China

Unsupervised learning technology has caught up with or even surpassed supervised learning technology in general object classification (GOC) and person re-identification (re-ID). However, it is found that the unsupervised learning of fine-grained visual classification (FGVC) is more challenging than GOC and person re-ID. In order to bridge the gap between unsupervised and supervised learning for FGVC, we investigate the essential factors for the performance gap. Furthermore, we propose a simple, effective, and practical method, termed as UFCL, to alleviate the gap. Three key issues are concerned and improved: First, we introduce a robust and powerful backbone, ResNet50-IBN, which has an ability of domain adaptation when we transfer imageNet pre-trained models to FGVC tasks. Next, we propose to introduce HDBSCAN instead of DBSCAN to do clustering, which can generate better clusters for adjacent categories with fewer hyper-parameters. Finally, we propose a weighted feature agent and its updating mechanism to do contrastive learning, which can improve the optimization process of learning the parameters of the network. The effectiveness of our UFCL is verified on four public FGVC datasets. Under the unsupervised FGVC setting, we achieve state-of-the-art results, and analyze the key factors and the important parameters to provide a practical guidance. © 2023, The Authors. All rights reserved.

关键词： image classification

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SEMICON: A Learning-to-hash Solution for Large-scale Fine-grained image Retrieval

arXiv

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arXiv 2022年

作者： Shen, Yang Sun, Xuhao Wei, Xiu-Shen Jiang, Qing-Yuan Yang, Jian School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of Integrated Services Networks Xidian University China State Key Laboratory for Novel Software Technology Nanjing University China Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security Nanjing University of Science and Technology China

In this paper, we propose Suppression-Enhancing Mask based attention and Interactive Channel transformatiON (SEMICON) to learn binary hash codes for dealing with large-scale fine-grained image retrieval tasks. In SEMICON, we first develop a suppression-enhancing mask (SEM) based attention to dynamically localize discriminative image regions. More importantly, different from existing attention mechanism simply erasing previous discriminative regions, our SEM is developed to restrain such regions and then discover other complementary regions by considering the relation between activated regions in a stage-by-stage fashion. In each stage, the interactive channel transformation (ICON) module is afterwards designed to exploit correlations across channels of attended activation tensors. Since channels could generally correspond to the parts of fine-grained objects, the part correlation can be also modeled accordingly, which further improves fine-grained retrieval accuracy. Moreover, to be computational economy, ICON is realized by an efficient two-step process. Finally, the hash learning of our SEMICON consists of both global- and local-level branches for better representing fine-grained objects and then generating binary hash codes explicitly corresponding to multiple levels. Experiments on five benchmark fine-grained datasets show our superiority over competing methods. Copyright © 2022, The Authors. All rights reserved.

关键词： image retrieval

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JobFormer: Skill-Aware Job Recommendation with Semantic-Enhanced Transformer

arXiv

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arXiv 2024年

作者： Guan, ZhiHao Yang, Jia-Qi Yang, Yang Zhu, Hengshu Li, Wenjie Xiong, Hui the Nanjing University of Science and Technology Nanjing210094 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Career Science Lab BOSS Zhipin Beijing100028 China the Hong Kong Polytechnic University Hong Kong the Artificial Intelligence Thrust The Hong Kong University of Science and Technology Guangzhou China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China

Job recommendation aims to provide potential talents with suitable job descriptions (JDs) consistent with their career trajectory, which plays an essential role in proactive talent recruitment. In real-world management scenarios, the available JD-user records always consist of JDs, user profiles, and click data, in which the user profiles are typically summarized as the user’s skill distribution for privacy reasons. Although existing sophisticated recommendation methods can be directly employed, effective recommendation still has challenges considering the information deficit of JD itself and the natural heterogeneous gap between JD and user profile. To address these challenges, we proposed a novel skill-aware recommendation model based on the designed semantic-enhanced transformer to parse JDs and complete personalized job recommendation. Specifically, we first model the relative items of each JD and then adopt an encoder with the local-global attention mechanism to better mine the intra-job and inter-job dependencies from JD tuples. Moreover, we adopt a two-stage learning strategy for skill-aware recommendation, in which we utilize the skill distribution to guide JD representation learning in the recall stage, and then combine the user profiles for final prediction in the ranking stage. Consequently, we can embed rich contextual semantic representations for learning JDs, while skill-aware recommendation provides effective JD-user joint representation for click-through rate (CTR) prediction. To validate the superior performance of our method for job recommendation, we present a thorough empirical analysis of large-scale real-world and public datasets to demonstrate its effectiveness and interpretability. Copyright © 2024, The Authors. All rights reserved.

关键词： User profile

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A Novel Initialization Method of Fixed Point Continuation for Recommendation Systems

SSRN

引用

SSRN 2022年

作者： Zhao, Jianli Zhang, Tianheng Sun, Qiuxia Huo, Huan Gong, Maoguo College of Computer Science & Engineering Shandong University of Science and Technology Qingdao China College of Mathematics and Systems Science Shandong University of Science and Technology Qingdao China Faculty of Engineering and Information Technology University of Technology Sydney Australia Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education Xidian University Xi'An710071 China

In recent years, the problem of matrix completion based on rank minimization has received widespread attention in machine learning. The tightest convex relaxation of this problem is the linearly constrained nuclear norm minimization. Fixed point continuation (FPC), as a representative nuclear norm relaxation matrix completion algorithm, has been proven to perform well in the theories and experiments. However, the traditional FPC algorithm initializes the matrix to be completed by zero, which does not make full use of the shrinkage characteristics of the singular value shrinkage operator on the matrix elements and the known field data information, then will lead to slow convergence and poor accuracy. Aiming at this problem, this paper analyzes the shrinkage properties of matrix elements in the iterative process of FPC algorithm. Combined with the known rating information in the recommendation systems, a new initialization method of overestimation based on FPC is proposed, and it is applied to the rating prediction in the recommendation systems. The experimental results show that the initialization method proposed in this paper greatly improves the algorithm efficiency and prediction accuracy. © 2022, The Authors. All rights reserved.

关键词： Recommender systems

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Maximizing Mutual Information Across Feature and Topology Views for Learning Graph Representations

arXiv

引用

arXiv 2021年

作者： Fan, Xiaolong Gong, Maoguo Wu, Yue Li, Hao The School of Electronic Engineering Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education Xidian University Shaanxi Province Xi’an China The School of Computer Science and Technology Xidian University Shaanxi Province Xi’an China

Recently, maximizing mutual information has emerged as a powerful method for unsupervised graph representation learning. The existing methods are typically effective to capture graph information from the topology view but consistently ignore the feature view. To circumvent this issue, we propose a novel approach by exploiting mutual information maximization across feature and topology views. Specifically, we first utilize a multi-view representation learning module to better capture both local and global information content across feature and topology views on graphs. To model the information shared by the feature and topology spaces, we then develop a common representation learning module via using mutual information maximization and reconstruction loss minimization. Here, minimizing reconstruction loss forces the model to learn the shared information of feature and topology spaces. To explicitly encourage diversity between graph representations from the same view, we also introduce a disagreement regularization to enlarge the distance between representations from the same view. Experiments on synthetic and real-world datasets demonstrate the effectiveness of integrating feature and topology views. In particular, compared with the previous supervised methods, our proposed method can achieve comparable or even better performance under the unsupervised representation and linear evaluation protocol. Copyright © 2021, The Authors. All rights reserved.

关键词： Graph neural networks

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