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SSRN 2023年

作者： Li, Zhongnian Xu, Mengting Xu, Xinzheng Zhang, Daoqiang School of Computer Science and Technology China University of Ming and Technogy Jiangsu Xuzhou221000 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing210000 China College of Computer Science and Technology Zhejiang University Zhejiang Hangzhou310000 China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Jiangsu Nanjing210000 China

Complementary Labels Learning (CLL) arises in many real-world tasks such as private questions classification and online learning, which aims to alleviate the annotation cost compared with standard supervised ***, most previous CLL algorithms were in a stable environment rather than an open and dynamic scenarios, where data collected from unseen augmented classes in the training process might emerge in the testing *** this paper, we propose a novel problem setting called Complementary Labels Learning with Augmented Classes (CLLAC), which brings the challenge that classifiers trained by complementary labels should not only be able to classify the instances from observed classes accurately, but also recognize the instance from the Augmented Classes in the testing phase. Specifically, by using unlabeled data, we propose an unbiased estimator of classification risk for CLLAC, which is guaranteed to be provably consistent. Moreover, we provide generalization error bound for proposed method which shows that the optimal parametric convergence rate is achieved for estimation error. Finally, the experimental results on several benchmark datasets verify the effectiveness of the proposed method. © 2023, The Authors. All rights reserved.

关键词： machine learning

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CE-AH: A contrast-enhanced attention hierarchical network for Alzheimer's disease diagnosis based on structural MRI

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pattern Recognition 2026年 169卷

作者： Tianxiang Wang Qun Dai Han Lu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing 211106 China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing 211106 China

Numerous deep learning-based methods utilizing structural magnetic resonance imaging (sMRI) have been developed for diagnosing Alzheimer's disease (AD). However, the majority of these methods overlook the localized nature of brain atrophy. Moreover, they often rely on a single scale, neglecting the global structural information. To address these issues concurrently, this study introduces a Contrast-Enhanced Attention Hierarchical (CE-AH) network for AD diagnosis. Initially, we employ pretraining through contrastive learning to bolster the network's feature extraction capabilities. Subsequently, we engineer a hierarchical model that integrates dual attention mechanisms to extract multi-scale discriminative features. To overcome the convergence challenges and training instability inherent in patch-based methods, we implement group normalization in place of batch normalization. The CE-AH model's performance is assessed on the ADNI dataset, yielding outstanding classification outcomes. Furthermore, experimental results indicate that the training process of our proposed model is remarkably stable.

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A lightweight multi-scale context network for salient object detection in optical remote sensing images

arXiv

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arXiv 2022年

作者： Lin, Yuhan Sun, Han Liu, Ningzhong Bian, Yetong Cen, Jun Zhou, Huiyu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom

Due to the more dramatic multi-scale variations and more complicated foregrounds and backgrounds in optical remote sensing images (RSIs), the salient object detection (SOD) for optical RSIs becomes a huge challenge. However, different from natural scene images (NSIs), the discussion on the optical RSI SOD task still remains scarce. In this paper, we propose a multi-scale context network, namely MSCNet, for SOD in optical RSIs. Specifically, a multi-scale context extraction module is adopted to address the scale variation of salient objects by effectively learning multi-scale contextual information. Meanwhile, in order to accurately detect complete salient objects in complex backgrounds, we design an attention-based pyramid feature aggregation mechanism for gradually aggregating and refining the salient regions from the multi-scale context extraction module. Extensive experiments on two benchmarks demonstrate that MSCNet achieves competitive performance with only 3.26M parameters. The code will be available at https://***/NuaaYH/MSCNet. © 2022, CC BY.

关键词： Extraction

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Nlkd: Using Coarse Annotations For Semantic Segmentation Based on Knowledge Distillation

Nlkd: Using Coarse Annotations For Semantic Segmentation Bas...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Dong Liang Yun Du Han Sun Liyan Zhang Ningzhong Liu Mingqiang Wei College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Collaborative Innovation Center of Novel Software Technology and Industrialization

ISBN: (纸本)9781728176055;9781728176062

Modern supervised learning relies on a large amount of training data, yet there are many noisy annotations in real datasets. For semantic segmentation tasks, pixel-level annotation noise is typically located at the edge of an object, while pixels within objects are fine-annotated. We argue the coarse annotations can provide instructive supervised information to guide model training rather than be discarded. This paper proposes a noise learning framework based on knowledge distillation NLKD, to improve segmentation performance on unclean data. It utilizes a teacher network to guide the student network that constitutes the knowledge distillation process. The teacher and student generate the pseudo-labels and jointly evaluate the quality of annotations to generate weights for each sample. Experiments demonstrate the effectiveness of NLKD, and we observe better performance with boundary-aware teacher networks and evaluation metrics. Furthermore, the proposed approach is model-independent and easy to implement, appropriate for integration with other tasks and models.

关键词： Training Annotations Semantics Supervised learning Training data Signal processing Noise robustness

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Learning from Positive and Unlabeled Data with Augmented Classes

SSRN

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SSRN 2023年

作者： Li, Zhongnian Yang, Liutao Ma, Zhongchen Sun, Tongfeng Xu, Xinzheng Zhang, Daoqiang School of Computer Science and Technology China University of Ming and Technogy Jiangsu Xuzhou221000 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing210000 China School of Computer Science and communications Engineering Jiangsu University Jiangsu Zhenjiang212013 China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Jiangsu Nanjing210000 China

Positive Unlabeled (PU) learning aims to learn a binary classifier from only positive and unlabeled data, which is utilized in many real-world scenarios. However, existing PU learning algorithms cannot deal with the real-world challenge in an open and changing scenario, where examples from unobserved augmented classes may emerge in the testing phase. In this paper, we propose an unbiased risk estimator for PU learning with Augmented Classes (PUAC) by utilizing unlabeled data from the augmented classes distribution, which can be easily collected in many real-world scenarios. Besides, we derive the estimation error bound for the proposed estimator, which provides a theoretical guarantee for its convergence to the optimal solution. Experiments on multiple realistic datasets demonstrate the effectiveness of proposed approach. © 2023, The Authors. All rights reserved.

关键词： Learning algorithms

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Better safe than sorry: preventing delusive adversaries with adversarial training 21

Better safe than sorry: preventing delusive adversaries with...

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Lue Tao Lei Feng Jinfeng Yi Sheng-Jun Huang Songcan Chen College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics and MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science Chongqing University JD AI Research

ISBN: (纸本)9781713845393

Delusive attacks aim to substantially deteriorate the test accuracy of the learning model by slightly perturbing the features of correctly labeled training examples. By formalizing this malicious attack as finding the worst-case training data within a specific ∞-Wasserstein ball, we show that minimizing adversarial risk on the perturbed data is equivalent to optimizing an upper bound of natural risk on the original data. This implies that adversarial training can serve as a principled defense against delusive attacks. Thus, the test accuracy decreased by delusive attacks can be largely recovered by adversarial training. To further understand the internal mechanism of the defense, we disclose that adversarial training can resist the delusive perturbations by preventing the learner from overly relying on non-robust features in a natural setting. Finally, we complement our theoretical findings with a set of experiments on popular benchmark datasets, which show that the defense withstands six different practical attacks. Both theoretical and empirical results vote for adversarial training when confronted with delusive adversaries.

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arXiv

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arXiv 2022年

作者： Ma, Zhongchen Chen, Songcan The School of Computer Science & communications Engineering Jiangsu University The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Zhenjiang212013 China The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics The MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing211106 China

—Similarity-based method gives rise to a new class of methods for multi-label learning and also achieves promising performance. In this paper, we generalize this method, resulting in a new framework for classification task. Specifically, we unite similarity-based learning and generalized linear models to achieve the best of both worlds. This allows us to capture interdependencies between classes and prevent from impairing performance of noisy classes. Each learned parameter of the model can reveal the contribution of one class to another, providing interpretability to some extent. Experiment results show the effectiveness of the proposed approach on multi-class and multi-label datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： machine learning

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Centralized Feature Pyramid for Object Detection

arXiv

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arXiv 2022年

作者： Quan, Yu Zhang, Dong Zhang, Liyan Tang, Jinhui The School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing211106 China

Visual feature pyramid has shown its superiority in both effectiveness and efficiency in a wide range of applications. However, the existing methods exorbitantly concentrate on the inter-layer feature interactions but ignore the intra-layer feature regulations, which are empirically proved beneficial. Although some methods try to learn a compact intra-layer feature representation with the help of the attention mechanism or the vision transformer, they ignore the neglected corner regions that are important for dense prediction tasks. To address this problem, in this paper, we propose a Centralized Feature Pyramid (CFP) for object detection, which is based on a globally explicit centralized feature regulation. Specifically, we first propose a spatial explicit visual center scheme, where a lightweight MLP is used to capture the globally long-range dependencies and a parallel learnable visual center mechanism is used to capture the local corner regions of the input images. Based on this, we then propose a globally centralized regulation for the commonly-used feature pyramid in a top-down fashion, where the explicit visual center information obtained from the deepest intra-layer feature is used to regulate frontal shallow features. Compared to the existing feature pyramids, CFP not only has the ability to capture the global long-range dependencies, but also efficiently obtain an all-round yet discriminative feature representation. Experimental results on the challenging MS-COCO validate that our proposed CFP can achieve the consistent performance gains on the state-of-the-art YOLOv5 and YOLOX object detection baselines. The code has been released at: CFPNet. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Coarse-to-fine Foreground Segmentation based on Co-occurrence Pixel-Block and Spatio-Temporal Attention Model

Coarse-to-fine Foreground Segmentation based on Co-occurrenc...

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International Conference on pattern Recognition

作者： Dong Liang Xinyu Liu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China

ISBN: (纸本)9781728188089;9781728188096

Foreground segmentation in dynamic scene is an important task in video surveillance. The unsupervised background subtraction method based on background statistics modeling has difficulties in updating. On the other hand, the supervised foreground segmentation method based on deep learning relies on the large-scale of accurately annotated training data, which limits its cross-scene performance. In this paper, we propose a foreground segmentation method from coarse to fine. First, a across-scenes trained Spatio-Temporal Attention Model (STAM) is used to achieve coarse segmentation, which does not require training on specific scene. Then the coarse segmentation is used as a reference to help Co-occurrence Pixel-Block Model (CPB) complete the fine segmentation, and at the same time help CPB to update its background model. This method is more flexible than those deep-learning-based methods which depends on the specific-scene training, and realizes the accurate online dynamic update of the background model. Experimental results on WallFlower and LIMU validate our method outperforms STAM, CPB and other methods of participating in comparison.

关键词： Training Deep learning Training data Video surveillance pattern recognition Task analysis

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ACRM: Attention Cascade R-CNN with Mix-NMS for Metallic Surface Defect Detection

ACRM: Attention Cascade R-CNN with Mix-NMS for Metallic Surf...

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International Conference on pattern Recognition

作者： Junting Fang Xiaoyang Tan Yuhui Wang College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China

Metallic surface defect detection is of great significance in quality control for production. However, this task is very challenging due to the noise disturbance, large appearance variation, and the ambiguous definition of the defect individual. Traditional image processing methods are unable to detect the damaged region effectively and efficiently. In this paper, we propose a new defect detection method, Attention Cascade R-CNN with Mix-NMS (ACRM), to classify and locate defects robustly. Three submodules are developed to achieve this goal: 1) a lightweight attention block is introduced, which can improve the ability in capture global and local feature both in the spatial and channel dimension; 2) we firstly apply the cascade R-CNN to our task, which exploits multiple detectors to sequentially refine the detection result robustly; 3) we introduce a new method named Mix Non-Maximum Suppression (Mix-NMS), which can significantly improve its ability in filtering the redundant detection result in our task. Extensive experiments on a real industrial dataset show that ACRM achieves state-of-the-art results compared to the existing methods, demonstrating the effectiveness and robustness of our detection method.

关键词： Filtering Quality control Production Detectors Feature extraction Robustness pattern recognition

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