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第32届中国控制与决策会议

作者： Yi Cheng Renyan Fang School of Electrical Engineering and Automation Tiangong University

Based on Simultaneous Localization and Mapping（SLAM） and deep learning,a SLAM system incorporating weak supervised learning semantic segmentation is proposed for navigation and positioning of mobile robots in dynamic environment.After obtaining the image from the camera,the weakly supervised learning semantic segmentation network SEC was used to segment the semantic information,and then the obtained semantic information was used to eliminate the dynamic feature points,so as to improve the accuracy of pose estimation of the system.Compared to traditional orb-slam2,this article shows an overall improvement in performance on publicly available high dynamic sequence data sets.The results show that after improvement,both absolute error,relative drift and rotational drift are significantly reduced,which proves that compared with orb-slam2,this method can improve the accuracy of pose estimation in dynamic environment.

关键词： Dynamic Environment ORB-SLAM2 weakly-supervised learning Semantic Segmentation

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Transformers in medical image analysis

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Intelligent Medicine 2023年第1期3卷 59-78页

作者： Kelei He Chen Gan Zhuoyuan Li Islem Rekik Zihao Yin Wen Ji Yang Gao Qian Wang Junfeng Zhang Dinggang Shen Medical School of Nanjing University NanjingJiangsu 210093China National Institute of Healthcare Data Science at Nanjing University NanjingJiangsu 210093China BASIRA Laboratory Faculty of Computer and Informatics EngineeringIstanbul Technical UniversityIstanbulTurkey School of Science and Engineering ComputingUniversity of DundeeUK State Key Laboratory for Novel Software Technology Nanjing UniversityNanjingJiangsu 210093China School of Biomedical Engineering ShanghaiTech UniversityShanghai 201210China Department of Research and Development Shanghai United Imaging Intelligence Co.Ltd.Shanghai 200030China Shanghai Clinical Research and Trial Center Shanghai 201703China

Transformers have dominated the field of natural language processing and have recently made an impact in the area of computer vision.In the field of medical image analysis,transformers have also been successfully used in to full-stack clinical applications,including image synthesis/reconstruction,registration,segmentation,detection,and diagnosis.This paper aimed to promote awareness of the applications of transformers in medical image analysis.Specifically,we first provided an overview of the core concepts of the attention mechanism built into transformers and other basic components.Second,we reviewed various transformer architectures tailored for medical image applications and discuss their limitations.Within this review,we investigated key challenges including the use of transformers in different learning paradigms,improving model efficiency,and coupling with other techniques.We hope this review would provide a comprehensive picture of transformers to readers with an interest in medical image analysis.

关键词： Transformer Medical image analysis Deep learning Diagnosis Registration Segmentation Image synthesis Multi-task learning Multi-modal learning weakly-supervised learning

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Visual Superordinate Abstraction for Robust Concept learning

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Machine Intelligence Research 2023年第1期20卷 79-91页

作者： Qi Zheng Chao-Yue Wang Dadong Wang Da-Cheng Tao University of Sydney Sydney 2008Australia JD Explore Academy Beijing 100176China DATA61 Commonwealth Scientific and Industrial Research OrganisationSydney 2122Australia

Concept learning constructs visual representations that are connected to linguistic semantics, which is fundamental to vision-language tasks. Although promising progress has been made, existing concept learners are still vulnerable to attribute perturbations and out-of-distribution compositions during inference. We ascribe the bottleneck to a failure to explore the intrinsic semantic hierarchy of visual concepts, e.g., {red, blue,···} ∈“color” subspace yet cube ∈“shape”. In this paper, we propose a visual superordinate abstraction framework for explicitly modeling semantic-aware visual subspaces(i.e., visual superordinates). With only natural visual question answering data, our model first acquires the semantic hierarchy from a linguistic view and then explores mutually exclusive visual superordinates under the guidance of linguistic hierarchy. In addition, a quasi-center visual concept clustering and superordinate shortcut learning schemes are proposed to enhance the discrimination and independence of concepts within each visual superordinate. Experiments demonstrate the superiority of the proposed framework under diverse settings, which increases the overall answering accuracy relatively by 7.5% for reasoning with perturbations and 15.6% for compositional generalization tests.

关键词： Concept learning visual question answering weakly-supervised learning multi-modal learning curriculum learning

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Attention Guided Enhancement Network for weakly supervised Semantic Segmentation

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Chinese Journal of Electronics 2023年第4期32卷 896-907页

作者： ZHANG Zhe WANG Bilin YU Zhezhou ZHAO Fengzhi College of Computer Science and Technology Jilin University Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry

weakly supervised semantic segmentation using only image-level labels is critical since it alleviates the need for expensive pixel-level labels. Most cuttingedge methods adopt two-step solutions that learn to produce pseudo-ground-truth using only image-level labels and then train off-the-shelf fully supervised semantic segmentation network with these pseudo labels. Although these methods have made significant progress, they also increase the complexity of the model and training. In this paper, we propose a one-step approach for weakly supervised image semantic segmentation—attention guided enhancement network(AGEN), which produces pseudopixel-level labels under the supervision of image-level labels and trains the network to generate segmentation masks in an end-to-end manner. Particularly, we employ class activation maps(CAM) produced by different layers of the classification branch to guide the segmentation branch to learn spatial and semantic information.However, the CAM produced by the lower layer can capture the complete object region but with many noises.Thus, the self-attention module is proposed to enhance object regions adaptively and suppress irrelevant object regions, further boosting the segmentation performance.Experiments on the Pascal VOC 2012 dataset demonstrate that AGEN outperforms alternative state-of-the-art weakly supervised semantic segmentation methods exclusively relying on image-level labels.

关键词： weakly-supervised learning Semantic segmentation Convolutional neural networks Self-attention mechanism End-to-end

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Deep graph cut network for weakly-supervised semantic segmentation

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Science China(Information Sciences) 2021年第3期64卷 57-68页

作者： Jiapei FENG Xinggang WANG Wenyu LIU School of Electronic Information and Communications Huazhong University of Science and Technology

The scarcity of fully-annotated data becomes the biggest obstacle that prevents many deep learning approaches from widely applied. weakly-supervised visual learning which can utilize inexact annotations is developed rapidly to remedy such a situation. In this paper, we study the weakly-supervised task achieving pixel-level semantic segmentation only with image-level labels as supervision. Different from other methods,our approach tries to transform the weakly-supervised visual learning problem into a semi-supervised visual learning problem and then utilizes semi-supervised learning methods to solve it. Utilizing this transformation,we can adopt effective semi-supervised methods to perform transductive learning with context information.In the semi-supervised learning module, we propose to use the graph cut algorithm to label more supervision from the activation seeds generated from a classification network. The generated labels can provide the segmentation model with effective supervision information; moreover, the graph cut module can benefit from features extracted by the segmentation model. Then, each of them updates and optimizes the other iteratively until convergence. Experiment results on PASCAL VOC and COCO benchmarks demonstrate the effectiveness of the proposed deep graph cut algorithm for weakly-supervised semantic segmentation.

关键词： semantic segmentation weakly-supervised learning semi-supervised learning graph cut

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Multi-Label Image Classification with Weak Correlation Prior

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CAAI Artificial Intelligence Research 2022年第1期1卷 79-92页

作者： Xiao Ouyang Ruidong Fan Hong Tao Chenping Hou Department of Systems Science National University of Defense TechnologyChangsha 410073China

Image classification is vital and basic in many data analysis domains.Since real-world images generally contain multiple diverse semantic labels,it amounts to a typical multi-label classification problem.Traditional multi-label image classification relies on a large amount of training data with plenty of labels,which requires a lot of human and financial costs.By contrast,one can easily obtain a correlation matrix of concerned categories in current scene based on the historical image data in other application scenarios.How to perform image classification with only label correlation priors,without specific and costly annotated labels,is an important but rarely studied problem.In this paper,we propose a model to classify images with this kind of weak correlation prior.We use label correlation to recapitulate the sample similarity,employ the prior information to decompose the projection matrix when regressing the label indication matrix,and introduce the L_(2,1) norm to select features for each image.Finally,experimental results on several image datasets demonstrate that the proposed model has distinct advantages over current state-of-the-art multi-label classification methods.

关键词： image recognition label correlation multi-label classification weakly-supervised learning

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