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2022 International Conference on Intelligent Systems, Communications, and computer Networks, ISCCN 2022

作者： Xu, Yangdongfang Yang, Guan Liu, Xiaoming Liu, Yang School of Computer Science Zhongyuan University of Technology Zhengzhou China Henan Key Laboratory on Public Opinion Intelligent Analysis Zhengzhou China Zhengzhou Key Laboratory of Text Processing and Image Understanding Zhengzhou China School of Telecommunications Engineering Xidian University Xi’an China

ISBN: (纸本)9781510657342

Generalized Zero-Shot Learning (GZSL) is characterized as a training process that comprises visual samples from seen classes and semantic samples from seen and unseen classes, followed by a testing process that classifies visual samples from seen and unseen classes. Existing zero-shot learning (ZSL) approaches suffer from domain shift and information loss issues as a result of class differences between visible and unseen classes, as well as uneven image distribution. In this study, a generalized zero-shot learning strategy based on dual latent space reconstruction (DLR-GZSL) is proposed. The method aims to establish a latent space of shared semantic and visual information, uses dual learning to align different modal representations to alleviate the domain shift problem, uses triplet loss to improve intra-class diversity and inter-class separability of the generated samples, and uses information bottleneck to retain as much valid generated feature information as possible to reduce information loss. Experiments on the CUB, SUN, AWA1, and AWA2 datasets reveal that the suggested method has more accurate than previous methods, demonstrating its effectiveness. © 2022 SPIE.

关键词： Zero-shot learning

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Clothed Human Performance Capture with a Double-layer Neural Radiance Fields

Clothed Human Performance Capture with a Double-layer Neural...

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2023 IEEE/CVF Conference on computer vision and Pattern Recognition, CVPR 2023

作者： Wang, Kangkan Zhang, Guofeng Cong, Suxu Yang, Jian Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of CAD&CG Zhejiang University China

This paper addresses the challenge of capturing performance for the clothed humans from sparse-view or monocular videos. Previous methods capture the performance of full humans with a personalized template or recover the garments from a single frame with static human poses. However, it is inconvenient to extract cloth semantics and capture clothing motion with one-piece template, while single frame-based methods may suffer from instable tracking across videos. To address these problems, we propose a novel method for human performance capture by tracking clothing and human body motion separately with a double-layer neural radiance fields (NeRFs). Specifically, we propose a double-layer NeRFs for the body and garments, and track the densely deforming template of the clothing and body by jointly optimizing the deformation fields and the canonical double-layer NeRFs. In the optimization, we introduce a physics-aware cloth simulation network which can help generate physically plausible cloth dynamics and body-cloth interactions. Compared with existing methods, our method is fully differentiable and can capture both the body and clothing motion robustly from dynamic videos. Also, our method represents the clothing with an independent NeRFs, allowing us to model implicit fields of general clothes feasibly. The experimental evaluations validate its effectiveness on real multi-view or monocular videos. ©2023 IEEE.

关键词： Multilayer neural networks

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CAT: a coarse-to-fine attention tree for semantic change detection

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Visual Intelligence 2023年第1期1卷 1-12页

作者： Wei, Xiu-Shen Xu, Yu-Yan Zhang, Chen-Lin Xia, Gui-Song Peng, Yu-Xin School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China State Key Laboratory of Integrated Services Networks Xidian University Xi’an China 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 Nanjing China 4Paradigm Inc Beijing China School of Computer Science and Institute of Artificial Intelligence Wuhan University Wuhan China Wangxuan Institute of Computer Technology Peking University Beijing China

Semantic change detection (SCD) and land cover mapping (LCM) are always treated as a dual task in the field of remote sensing. However, due to diverse real-world scenarios, many SCD categories are not easy to be clearly recognized, such as “water-vegetation” and “water-tree”, which can be regarded as fine-grained differences. In addition, even a single LCM category is usually difficult to define. For instance, some “vegetation” categories with litter vegetation coverage are easily confused with the general “ground” category. SCD/LCM becomes challenging under both challenges of its fine-grained nature and label ambiguity. In this paper, we tackle the SCD and LCM tasks simultaneously by proposing a coarse-to-fine attention tree (CAT) model. Specifically, it consists of an encoder, a decoder and a coarse-to-fine attention tree module. The encoder-decoder structure extracts the high-level features from input multi-temporal images first and then reconstructs them to return SCD and LCM predictions. Our coarse-to-fine attention tree, on the one hand, utilizes the tree structure to better model a hierarchy of categories by predicting the coarse-grained labels first and then predicting the fine-grained labels later. On the other hand, it applies the attention mechanism to capture discriminative pixel regions. Furthermore, to address label ambiguity in SCD/LCM, we also equip a label distribution learning loss upon our model. Experiments on the large-scale SECOND dataset justify that the proposed CAT model outperforms state-of-the-art models. Moreover, various ablation studies have demonstrated the effectiveness of tailored designs in the CAT model for solving semantic change detection problems. © The Author(s) 2023.

关键词： Attention tree Coarse-to-fine Fine-grained recognition Label ambiguity Semantic change detection

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CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Wang, Jianing Hua, Zheng Zhang, Wan Hao, Shengjia Yao, Yuqiong Gong, Maoguo Xidian University Key Laboratory of Collaborative Intelligence Systems Ministry of Education School of Computer Science and Technology Xi’an710071 China Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China School of Artificial Intelligence Xi’an710071 China Xidian University Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xi’an710071 China

Memory stability and learning flexibility in continual learning (CL) is a core challenge for cross-scene Hyperspectral Anomaly Detection (HAD) task. Biological neural networks can actively forget history knowledge that conflicts with the learning of new experiences by regulating learning-triggered synaptic expansion and synaptic convergence. Inspired by this phenomenon, we propose a novel Biologically-Inspired Continual Learning Generative Adversarial Network (CL-BioGAN) for augmenting continuous distribution fitting ability for cross-domain HAD task, where Continual Learning Bio-inspired Loss (CL-Bio Loss) and self-attention Generative Adversarial Network (BioGAN) are incorporated to realize forgetting history knowledge as well as involving replay strategy in the proposed BioGAN. Specifically, a novel Bio-Inspired Loss composed with an Active Forgetting Loss (AF Loss) and a CL loss is designed to realize parameters releasing and enhancing between new task and history tasks from a Bayesian perspective. Meanwhile, BioGAN loss with L2-Norm enhances self-attention (SA) to further balance the stability and flexibility for better fitting background distribution for open scenario HAD (OHAD) tasks. Experiment results underscore that the proposed CL-BioGAN can achieve more robust and satisfying accuracy for cross-domain HAD with fewer parameters and computation cost. This dual contribution not only elevates CL performance but also offers new insights into neural adaptation mechanisms in OHAD task. © 1980-2012 IEEE.

关键词： Training Continuing education History Biology Knowledge engineering Generative adversarial networks Bayes methods Hyperspectral imaging Fitting Biological system modeling

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Deep Learning Methods for Ship Classification: From Visible to Infrared images 5

Deep Learning Methods for Ship Classification: From Visible ...

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5th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2023

作者： Liu, Tianci Qin, Hengjia Zhan, Zhuo Liu, Yunpeng Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Chinese Academy of Sciences Key Laboratory of Opto-Electronic Information Processing Shenyang110016 China Key Laboratory of Image Understanding and Computer Vision Liaoning Province Shenyang110016 China The Third Military Representative Office of the Air Force Equipment Department in Shenyang Region Liaoning Province Shenyang110027 China

ISBN: (纸本)9798350357950

Deep learning methods have achieved excellent performances on visual tasks of target recognition and classification. The rapid development of autonomous seafaring vessels comes up with the requirement to recognize other maritime ships day and night. However, the recognition of ships based on the deep neural networks may not always access the results as expectation when the ships are under the nighttime environment. To this issue, we consider the ship recognition task under the deep learning framework with paired visible images and infrared images. In this article, we propose an end-to-end convolutional network based on visible images and infrared images of the autonomous seafaring vessels. To demonstrate the effectiveness of our model, we choose the V AIS dataset to test the performance of classifying the maritime ships. Experimental results show that the proposed network outperforms the state-of-the-art methods based on the V AIS database. © 2023 IEEE.

关键词： deep learning infrared images neural network Ship recognition

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Autoencoder-Based Latent Block-Diagonal Representation for Subspace Clustering

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IEEE Transactions on Cybernetics 2022年第6期52卷 5408-5418页

作者： Xu, Yesong Chen, Shuo Li, Jun Han, Zongyan Yang, Jian Nanjing University of Science and Technology PCA Laboratory Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Nanjing210094 China Nanjing University of Science and Technology Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing210094 China RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

Block-diagonal representation (BDR) is an effective subspace clustering method. The existing BDR methods usually obtain a self-expression coefficient matrix from the original features by a shallow linear model. However, the underlying structure of real-world data is often nonlinear, thus those methods cannot faithfully reflect the intrinsic relationship among samples. To address this problem, we propose a novel latent BDR (LBDR) model to perform the subspace clustering on a nonlinear structure, which jointly learns an autoencoder and a BDR matrix. The autoencoder, which consists of a nonlinear encoder and a linear decoder, plays an important role to learn features from the nonlinear samples. Meanwhile, the learned features are used as a new dictionary for a linear model with block-diagonal regularization, which can ensure good performances for spectral clustering. Moreover, we theoretically prove that the learned features are located in the linear space, thus ensuring the effectiveness of the linear model using self-expression. Extensive experiments on various real-world datasets verify the superiority of our LBDR over the state-of-the-art subspace clustering approaches. © 2013 IEEE.

关键词： Decoding

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Collaborative Weighting for Graph Convolutional Networks

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第2期8卷 432-447页

作者： Chen, Yong Xie, Xiao-Zhu Weng, Wei Zhang, Shan-Dan Li, Tong College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China College of Computer and Information Engineering Xiamen University of Technology Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China

Graph neural network (GNN), as a powerful method for graph representation, has attracted extensive research interest. Recently, Graph Convolutional Network (GCN) and Graph Attention Network (GAT) have shown superior performance on graph node classification and are considered as the most promising frameworks. Both GAT and GCN use a weighting mechanism in the information aggregation process. However, GCN is weighted by topological structure, while the node content is still unemployed. GAT is weighted by node content, while the topological structure remains being ignored. These weighting mechanism, which does not make full use of topology and node content, is not conducive to graph representation. In this paper, we propose a "Collaborative Weighting For Graph Convolutional Network" (CWGCN) that combines the advantages of GCN and GAT. Specifically, its weighting mechanism not only considers the topology of the graph, but also the node content. With this weighting method for representation learning, CWGCN achieves encouraging performance on a number of citation datasets in node classification. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Classification (of information)

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Topic Embedded Representation Enhanced Variational Wasserstein Autoencoder for Text Modeling 5

Topic Embedded Representation Enhanced Variational Wasserste...

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5th IEEE International Conference on Electronics Technology, ICET 2022

作者： Xiang, Zheng Liu, Xiaoming Yang, Guan Liu, Yang Zhongyuan University of Technology School of Computer Science Zhengzhou China Henan Key Laboratory on Public Opinion Intelligent Analysis Zhengzhou China Key Laboratory of text processing and image understanding Zhengzhou China Xidian University State Key Laboratory of Integrated Services Networks Xi'an China Shandong University Key Lab of Cryptologic Technology and Information Security Ministry of Education Xi'an China

ISBN: (纸本)9781665485081

Variational Autoencoder (VAE) is now popular in modeling and language generation tasks, which need to pay attention to the diversity of generation results. The existing models are insufficient in capturing the built-in relationships between topic representation and sequential words. At the same time, there is a massive contradiction between the commonly used simple Gaussian prior and the actual complex distribution of languages. To address the above problems, we introduce a hybrid Wasserstein Autoencoder (WAE) with Topic Embedded Representation (TER) for modeling. TER is obtained through an embedding-based topic model and can capture the dependencies and semantic similarities between topics and words. In this case, the learned latent variable has rich semantic knowledge with the help of TER and is easier to explain and control. Our experiments show that our method is competitive with other VAEs in modeling. © 2022 IEEE.

关键词： Semantics

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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...

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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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Interactive Semantic Segmentation With Weak Supervision 22

Interactive Semantic Segmentation With Weak Supervision

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8th International Conference on Computing and Artificial Intelligence, ICCAI 2022

作者： Gong, Lei Wang, Da-Han Wu, Yun Ye, Hai-Li Zhu, Chen-Yan School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China Medical Diagnostic Systems Co. Ltd. Xiamen361000 China

ISBN: (纸本)9781450396110

At present, the most advanced semantic segmentation model training mainly relies on pixel-level annotation, that is, annotating the category of each pixel of an image. Such annotation usually is time-consuming and expensive, especially for special applications that require expert annotation. The weakly-supervised segmentation method using the point-level supervision information has been investigated which however has great problems that the supervision information is quite limited and the performance is far from fully supervised methods. In this paper, we proposes an novelty interactive image segmentation method based on weak supervision, which allows multiple feedbacks of easily obtained weakly supervised information and improves the efficiency of utility of the supervision information. In the downstream task (interactive image segmentation), supervised information at the point level is used for many times, which makes the connection between pixels in the upstream task become closer and improves the segmentation accuracy. First, image-level tags are used to train the classification network. Then the pseudo-semantic labels are generated and put into the interactive segmentation network for training, and an almost completely supervised CNN is obtained, which further improves the performance and provides operability for human-computer interaction. The proposed method achieves promising semantic segmentation results that are close to those obtained by strongly supervised segmentation methods on the PASCAL VOC 2012 datasets. © 2022 ACM.

关键词： Semantic Segmentation

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