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Loop Closure Detection via Locality Preserving matching With Global Consensus

IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 411-426页

作者： jiayi ma Kaining Zhang Junjun Jiang IEEE the Electronic Information School Wuhan UniversityWuhan 430072China the School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China

A critical component of visual simultaneous localization and mapping is loop closure detection(LCD),an operation judging whether a robot has come to a pre-visited ***,given a query image(i.e.,the latest view observed by the robot),it proceeds by first exploring images with similar semantic information,followed by solving the relative relationship between candidate pairs in the 3D *** this work,a novel appearance-based LCD system is ***,candidate frame selection is conducted via the combination of Superfeatures and aggregated selective match kernel(ASMK).We incorporate an incremental strategy into the vanilla ASMK to make it applied in the LCD *** is demonstrated that this setting is memory-wise efficient and can achieve remarkable *** dig up consistent geometry between image pairs during loop closure verification,we propose a simple yet surprisingly effective feature matching algorithm,termed locality preserving matching with global consensus(LPM-GC).The major objective of LPM-GC is to retain the local neighborhood information of true feature correspondences between candidate pairs,where a global constraint is further designed to effectively remove false correspondences in challenging sceneries,e.g.,containing numerous repetitive ***,we derive a closed-form solution that enables our approach to provide reliable correspondences within only a few *** performance of the proposed approach has been experimentally evaluated on ten publicly available and challenging *** show that our method can achieve better performance over the state-of-the-art in both feature matching and LCD *** have released our code of LPM-GC at https://***/jiayi-ma/LPM-GC.

关键词： Feature matching locality preserving matching loop closure detection SLAM

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Contrastive Learning for Blind Super-Resolution via A Distortion-Specific Network

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IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 78-89页

作者： Xinya Wang jiayi ma Junjun Jiang the Electronic Information School Wuhan UniversityWuhan 430072China IEEE the School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China

Previous deep learning-based super-resolution(SR)methods rely on the assumption that the degradation process is predefined(e.g.,bicubic downsampling).Thus,their performance would suffer from deterioration if the real degradation is not consistent with the *** deal with real-world scenarios,existing blind SR methods are committed to estimating both the degradation and the super-resolved image with an extra loss or iterative ***,degradation estimation that requires more computation would result in limited SR performance due to the accumulated estimation *** this paper,we propose a contrastive regularization built upon contrastive learning to exploit both the information of blurry images and clear images as negative and positive samples,*** regularization ensures that the restored image is pulled closer to the clear image and pushed far away from the blurry image in the representation ***,instead of estimating the degradation,we extract global statistical prior information to capture the character of the *** the coupling between the degradation and the low-resolution image,we embed the global prior into the distortion-specific SR network to make our method adaptive to the changes of *** term our distortion-specific network with contrastive regularization as *** extensive experiments on synthetic and realworld scenes demonstrate that our lightweight CRDNet surpasses state-of-the-art blind super-resolution approaches.

关键词： Blind super-resolution contrastive learning deep learning image super-resolution(SR)

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Domain Adaptive Semantic Segmentation via Entropy-Ranking and Uncertain Learning-Based Self-Training

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IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1524-1527页

作者： Chengli Peng jiayi ma Electronic Information School Wuhan UniversityWuhan 430072China

Dear Editor,This letter develops two new self-training strategies for domain adaptive semantic segmentation,which formulate self-training into the processes of mining more training samples and reducing influence of th... 详细信息

关键词： false mining entropy

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SuperFusion: A Versatile Image Registration and Fusion Network with Semantic Awareness

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IEEE/CAA Journal of Automatica Sinica 2022年第12期9卷 2121-2137页

作者： Linfeng Tang Yuxin Deng Yong ma Jun Huang jiayi ma Electronic Information School Wuhan UniversityWuhan 430072China IEEE

Image fusion aims to integrate complementary information in source images to synthesize a fused image comprehensively characterizing the imaging scene. However, existing image fusion algorithms are only applicable to strictly aligned source images and cause severe artifacts in the fusion results when input images have slight shifts or deformations. In addition,the fusion results typically only have good visual effect, but neglect the semantic requirements of high-level vision *** study incorporates image registration, image fusion, and semantic requirements of high-level vision tasks into a single framework and proposes a novel image registration and fusion method, named Super Fusion. Specifically, we design a registration network to estimate bidirectional deformation fields to rectify geometric distortions of input images under the supervision of both photometric and end-point constraints. The registration and fusion are combined in a symmetric scheme, in which while mutual promotion can be achieved by optimizing the naive fusion loss, it is further enhanced by the mono-modal consistent constraint on symmetric fusion outputs. In addition, the image fusion network is equipped with the global spatial attention mechanism to achieve adaptive feature integration. Moreover, the semantic constraint based on the pre-trained segmentation model and Lovasz-Softmax loss is deployed to guide the fusion network to focus more on the semantic requirements of high-level vision tasks. Extensive experiments on image registration, image fusion,and semantic segmentation tasks demonstrate the superiority of our Super Fusion compared to the state-of-the-art *** source code and pre-trained model are publicly available at https://***/Linfeng-Tang/Super Fusion.

关键词： Global spatial attention image fusion image registration mutual promotion semantic awareness

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Hyperspectral Image Super-Resolution Meets Deep Learning:A Survey and Perspective

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1668-1691页

作者： Xinya Wang Qian Hu Yingsong Cheng jiayi ma the Electronic Information School Wuhan UniversityWuhan 430072China IEEE

Hyperspectral image super-resolution,which refers to reconstructing the high-resolution hyperspectral image from the input low-resolution observation,aims to improve the spatial resolution of the hyperspectral image,which is beneficial for subsequent *** development of deep learning has promoted significant progress in hyperspectral image super-resolution,and the powerful expression capabilities of deep neural networks make the predicted results more ***,several latest deep learning technologies have made the hyperspectral image super-resolution method ***,a comprehensive review and analysis of the latest deep learning methods from the hyperspectral image super-resolution perspective is *** this end,in this survey,we first introduce the concept of hyperspectral image super-resolution and classify the methods from the perspectives with or without auxiliary ***,we review the learning-based methods in three categories,including single hyperspectral image super-resolution,panchromatic-based hyperspectral image super-resolution,and multispectral-based hyperspectral image ***,we summarize the commonly used hyperspectral dataset,and the evaluations for some representative methods in three categories are performed qualitatively and ***,we briefly introduce several typical applications of hyperspectral image super-resolution,including ground object classification,urban change detection,and ecosystem ***,we provide the conclusion and challenges in existing learning-based methods,looking forward to potential future research directions.

关键词： Deep learning hyperspectral image image fusion image super-resolution survey

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BaMBNet:A Blur-Aware Multi-Branch Network for Dual-Pixel Defocus Deblurring

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IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 878-892页

作者： Pengwei Liang Junjun Jiang Xianming Liu jiayi ma School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China Electronic Information School Wuhan UniversityWuhan 430072China IEEE

Reducing the defocus blur that arises from the finite aperture size and short exposure time is an essential problem in computational *** is very challenging because the blur kernel is spatially varying and difficult to estimate by traditional *** to its great breakthrough in low-level tasks,convolutional neural networks(CNNs)have been introdu-ced to the defocus deblurring problem and achieved significant ***,previous methods apply the same learned kernel for different regions of the defocus blurred images,thus it is difficult to handle nonuniform blurred *** this end,this study designs a novel blur-aware multi-branch network(Ba-MBNet),in which different regions are treated *** particular,we estimate the blur amounts of different regions by the internal geometric constraint of the dual-pixel(DP)data,which measures the defocus disparity between the left and right *** on the assumption that different image regions with different blur amounts have different deblurring difficulties,we leverage different networks with different capacities to treat different image ***,we introduce a meta-learning defocus mask generation algorithm to assign each pixel to a proper *** this way,we can expect to maintain the information of the clear regions well while recovering the missing details of the blurred *** quantitative and qualitative experiments demonstrate that our BaMBNet outperforms the state-of-the-art(SOTA)*** the dual-pixel defocus deblurring(DPD)-blur dataset,the proposed BaMBNet achieves 1.20 dB gain over the previous SOTA method in term of peak signal-to-noise ratio(PSNR)and reduces learnable parameters by 85%.The details of the code and dataset are available at https://***/junjun-jiang/BaMBNet.

关键词： Blur kernel convolutional neural networks(CNNs) defocus deblurring dual-pixel(DP)data meta-learning

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Time Highlighted Multi-Interest Network for Sequential Recommendation

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Computers, materials & Continua 2023年第9期76卷 3569-3584页

作者： jiayi ma Tianhao Sun Xiaodong Zhang College of Computer Science Chongqing UniversityChongqing400044China

Sequential recommendation based on amulti-interest framework aims to analyze different aspects of interest based on historical interactions and generate predictions of a user’s potential interest in a list of *** existing methods only focus on what are themultiple interests behind interactions but neglect the evolution of user interests over *** explore the impact of temporal dynamics on interest extraction,this paper explicitly models the timestamp with amulti-interest network and proposes a time-highlighted network to learn user preferences,which considers not only the interests at different moments but also the possible trends of interest over *** specifically,the time intervals between historical interactions and prediction moments are first mapped to ***,a time-attentive aggregation layer is designed to capture the trends of items in the sequence over time,where the time intervals are seen as additional information to distinguish the importance of different ***,the learned items’transition trends are aggregated with the items themselves by a gated ***,a self-attention network is deployed to capture multiple interests with the obtained temporal information *** experiments are carried out based on three real-world datasets and the results convincingly establish the superiority of the proposed method over other state-of-the-art baselines in terms of model performance.

关键词： Recommender system temporal dynamics multi-interest network trends attention mechanism

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Loop Closure Detection With Reweighting Net VLAD and Local Motion and Structure Consensus

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IEEE/CAA Journal of Automatica Sinica 2022年第6期9卷 1087-1090页

作者： Kaining Zhang jiayi ma Junjun Jiang Electronic Information School Wuhan UniversityWuhan 430072China IEEE School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China

Dear Editor,Loop closure detection(LCD)is an important module in simultaneous localization and mapping(SLAM).In this letter,we address the LCD task from the semantic aspect to the geometric *** this end,a network termed as AttentionNetVLAD which can simultaneously extract global and local features is *** leverages attentive selection for local features,coupling with reweighting the soft assignment in NetVLAD via the attention map for global *** a query image,candidate frames are first identified coarsely by retrieving similar global features in the database via hierarchical navigable small world(HNSW).As global features mainly summarize the semantic information of images and lead to compact representation,information about spatial arrangement of visual elements is lost.

关键词： Net hierarchical arrangement

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Cas-FNE: Cascaded Face Normal Estimation

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2423-2434页

作者： Meng Wang Jiawan Zhang jiayi ma Xiaojie Guo the Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems the School of Computer Science and Technology Tiangong University IEEE the College of Intelligence and Computing Tianjin University the Electronic Information School Wuhan University

Capturing high-fidelity normals from single face images plays a core role in numerous computer vision and graphics applications. Though significant progress has been made in recent years, how to effectively and efficiently explore normal priors remains challenging. Most existing approaches depend on the development of intricate network architectures and complex calculations for in-the-wild face images. To overcome the above issue, we propose a simple yet effective cascaded neural network,called Cas-FNE, which progressively boosts the quality of predicted normals with marginal model parameters and computational cost. Meanwhile, it can mitigate the imbalance issue between training data and real-world face images due to the progressive refinement mechanism, and thus boost the generalization ability of the model. Specifically, in the training phase, our model relies solely on a small amount of labeled data. The earlier prediction serves as guidance for following refinement. In addition, our shared-parameter cascaded block employs a recurrent mechanism, allowing it to be applied multiple times for optimization without increasing network parameters. Quantitative and qualitative evaluations on benchmark datasets are conducted to show that our Cas-FNE can faithfully maintain facial details and reveal its superiority over state-of-the-art methods. The code is available at https://***/AutoHDR/***.

关键词： Training Graphics Computational modeling Neural networks Training data Predictive models Network architecture Data models Faces Optimization

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SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1200-1217页

作者： jiayi ma Linfeng Tang Fan Fan Jun Huang Xiaoguang Mei Yong ma The authors are with the Electronic Information School Wuhan UniversityWuhan 430072China IEEE

This study proposes a novel general image fusion framework based on cross-domain long-range learning and Swin Transformer,termed as *** the one hand,an attention-guided cross-domain module is devised to achieve sufficient integration of complementary information and global *** specifically,the proposed method involves an intra-domain fusion unit based on self-attention and an interdomain fusion unit based on cross-attention,which mine and integrate long dependencies within the same domain and across *** long-range dependency modeling,the network is able to fully implement domain-specific information extraction and cross-domain complementary information integration as well as maintaining the appropriate apparent intensity from a global *** particular,we introduce the shifted windows mechanism into the self-attention and cross-attention,which allows our model to receive images with arbitrary *** the other hand,the multi-scene image fusion problems are generalized to a unified framework with structure maintenance,detail preservation,and proper intensity ***,an elaborate loss function,consisting of SSIM loss,texture loss,and intensity loss,drives the network to preserve abundant texture details and structural information,as well as presenting optimal apparent *** experiments on both multi-modal image fusion and digital photography image fusion demonstrate the superiority of our SwinFusion compared to the state-of-theart unified image fusion algorithms and task-specific *** code and pre-trained weights can be accessed at https://***/Linfeng-Tang/SwinFusion.

关键词： Cross-domain long-range learning image fusion Swin transformer

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