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Size-Invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection

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arXiv 2024年

作者： Li, Feiran Xu, Qianqian Bao, Shilong Yang, Zhiyong Cong, Runmin Cao, Xiaochun Huang, Qingming Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China School of Control Science and Engineering Shandong University Jinan China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Sun Yat-Sen University Shenzhen Campus China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

Tigc-Net: Transformer-Improved Graph Convolution Network for Spatio-Temporal Prediction

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SSRN

SSRN 2022年

作者： Chen, Kai Yang, Chunfeng Zhou, Zhengyuan Liu, Yao Ji, Tianjiao Sun, Weiya Chen, Yang School of Cyber Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing210096 China The College of Software Engineering Southeast University Nanjing210096 China Laboratory of Image Science and Technology The School of Computer Science and Engineering Southeast University Nanjing210096 China Jiangsu Key Laboratory of Molecular and Functional Imaging Department of Radiology Zhongda Hospital Southeast University Nanjing210009 China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Nanjing210096 China NHC Key Laboratory of Medical Virology and Viral Diseases National Institute for Viral Disease Control and Prevention Chinese Center for Disease Control and Prevention Beijing China Beijing Institute of Tracking and Communication Technology Beijing100094 China

Modeling spatio-temporal sequences is an important topic yet challenging for existing neural networks. Most of the current spatio-temporal sequence prediction methods usually capture features separately in temporal and spatial dimensions or employ multiple mutually independent local spatio-temporal graphs to represent a spatio-temporal sequence. The first kind of method mentioned above is difficult to mine the complex spatio-temporal correlations, while the other is limited for the accuracy of long-term predictions. To handle these issues, this paper proposes a Transformer-Improved Graph Convolution Network for spatio-temporal prediction. Specifically, the temporal location encoding method is exploited to derive the spatio-temporal characteristics of the sequence utilizing a spatio-temporal feature fusion network. In addition, a spatio-temporal attention network is developed to enhance the spatio-temporal correlation of the sequence, and the dynamic spatial features of sequence are further extracted through the adaptive graph convolution network. A private dataset and a public dataset are employed to demonstrate the performance of the proposed TIGC-Net. The qualitative and quantitative results show that the proposed TIGC-Net can extract dynamic spatiotemporal properties more effectively, enhance the spatio-temporal correlation of sequences and improve the prediction accuracy compared with four stateof-the-art. © 2022, The Authors. All rights reserved.

关键词： Convolution

Dynamic Ca2+ sensitivity of BK channels

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Dynamic Ca2+ sensitivity of BK channels

The 10th Chinese Symposium on Calcium Signaling(第十届全国钙信号和细胞功能研讨会)

作者： Pan-Pan Hou Feng Xiao Hao-Wen Liu Ming Yuchi Guo-Hui Zhang Ying Wu Wei Wang Wen-Ping Zeng Ming-Yue Ding Zheng-Xing Wu Jian-Ming Cui Jiu-Ping Ding Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Department of Biomedical Engineering College of Life Science and Technology Wuhan Hubei China Department of pharmacology Soochow University college of pharmaceutical Sciences Suzhou 215123China Key Laboratory of Molecular Biophysics Huazhong University of Science and Technology Ministry of Education College of Life Science and Technology Wuhan Hubei China Department of Biomedical Engineering Center for the Investigation of Membrane Excitability DisordersCardiac Bioelectricity and Arrhythmia Center Washington University St Louis MO 63130 USA

Adaptive Fuzzy Tracking control with Global Prescribed-Time Prescribed Performance for Uncertain Strict-Feedback Nonlinear Systems

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arXiv 2022年

作者： Mao, Bing Wu, Xiaoqun Liu, Hui Xu, Yuhua Lü, Jinhu The School of Mathematics and Statistics Wuhan University Hubei430072 China Hubei Key Laboratory of Computational Science Wuhan University Hubei430072 China Research Center of Complex Network Wuhan University Hubei430072 China The School of Automation Huazhong University of Science and Technology Hubei430074 China The Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Hubei430074 China The School of Statistics and Data Science Nanjing Audit University Jiangsu211815 China The School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

Adaptive fuzzy control strategies are established to achieve global prescribed performance with prescribed-time convergence for strict-feedback systems with mismatched uncertainties and unknown nonlinearities. Firstly, to quantify the transient and steady performance constraints of the tracking error, a class of prescribed-time prescribed performance functions are designed, and a novel error transformation function is introduced to remove the initial value constraints and solve the singularity problem in existing works. Secondly, based on dynamic surface control methods, controllers with or without approximating structures are established to guarantee that the tracking error achieves prescribed transient performance and converges into a prescribed bounded set within prescribed time. In particular, the settling time and initial value of the prescribed performance function are completely independent of initial conditions of the tracking error and system parameters, which improves existing results. Moreover, with a novel Lyapunov-like energy function, not only the differential explosion problem frequently occurring in backstepping techniques is solved, but the drawback of the semi-global boundedness of tracking error induced by dynamic surface control can be overcome. The validity and effectiveness of the main results are verified by numerical simulations on practical examples. Copyright © 2022, The Authors. All rights reserved.

关键词： Fuzzy control

PUGAN: Physical Model-Guided Underwater image Enhancement Using GAN with Dual-Discriminators

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arXiv 2023年

作者： Cong, Runmin Yang, Wenyu Zhang, Wei Li, Chongyi Guo, Chun-Le Huang, Qingming Kwong, Sam Institute of Information Science Beijing Jiaotong University Beijing100044 China School of Control Science and Engineering Shandong University Jinan250061 China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan250061 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China College of Computer Science Nankai University Tianjin300350 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518055 China Department of Computer Science City University of Hong Kong Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

Due to the light absorption and scattering induced by the water medium, underwater images usually suffer from some degradation problems, such as low contrast, color distortion, and blurring details, which aggravate the difficulty of downstream underwater understanding tasks. Therefore, how to obtain clear and visually pleasant images has become a common concern of people, and the task of underwater image enhancement (UIE) has also emerged as the times require. Among existing UIE methods, Generative Adversarial Networks (GANs) based methods perform well in visual aesthetics, while the physical model-based methods have better scene adaptability. Inheriting the advantages of the above two types of models, we propose a physical model-guided GAN model for UIE in this paper, referred to as PUGAN. The entire network is under the GAN architecture. On the one hand, we design a Parameters Estimation subnetwork (Par-subnet) to learn the parameters for physical model inversion, and use the generated color enhancement image as auxiliary information for the Two-Stream Interaction Enhancement subnetwork (TSIE-subnet). Meanwhile, we design a Degradation Quantization (DQ) module in TSIE-subnet to quantize scene degradation, thereby achieving reinforcing enhancement of key regions. On the other hand, we design the Dual-Discriminators for the style-content adversarial constraint, promoting the authenticity and visual aesthetics of the results. Extensive experiments on three benchmark datasets demonstrate that our PUGAN outperforms state-of-the-art methods in both qualitative and quantitative metrics. The code and results can be found from the link of https://***/proj_***. © 2023, CC BY-NC-SA.

关键词： Discriminators

Camouflaged Object Detection via Context-aware Cross-level Fusion

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arXiv 2022年

作者： Chen, Geng Liu, Si-Jie Sun, Yu-Jia Ji, Ge-Peng Wu, Ya-Feng Zhou, Tao Northwestern Polytechnical University Xi’an710072 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering China Data Processing Center School of Power and Energy China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China School of Computer Science Inner Mongolia University Hohhot010021 China Artificial Intelligence Institute School of Computer Science Wuhan University Wuhan430072 China

Camouflaged object detection (COD) aims to identify the objects that conceal themselves in natural scenes. Accurate COD suffers from a number of challenges associated with low boundary contrast and the large variation of object appearances, e.g., object size and shape. To address these challenges, we propose a novel Context-aware Cross-level Fusion Network (C2F-Net), which fuses context-aware cross-level features for accurately identifying camouflaged objects. Specifically, we compute informative attention coefficients from multi-level features with our Attention-induced Cross-level Fusion Module (ACFM), which further integrates the features under the guidance of attention coefficients. We then propose a Dual-branch Global Context Module (DGCM) to refine the fused features for informative feature representations by exploiting rich global context information. Multiple ACFMs and DGCMs are integrated in a cascaded manner for generating a coarse prediction from high-level features. The coarse prediction acts as an attention map to refine the low-level features before passing them to our Camouflage Inference Module (CIM) to generate the final prediction. We perform extensive experiments on three widely used benchmark datasets and compare C2F-Net with state-of-the-art (SOTA) models. The results show that C2F-Net is an effective COD model and outperforms SOTA models remarkably. Further, an evaluation on polyp segmentation datasets demonstrates the promising potentials of our C2F-Net in COD downstream applications. Our code is publicly available at: https://***/Ben57882/C2FNet-TSCVT. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

Metric Learning Based Rolling Bearing Faults Diagnosis with Curvelet Transform

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Journal of Physics: Conference Series 2019年第1期1229卷

作者： Ziming Lu Jiang-Wen Xiao Zhengyi Huang School of Automation Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control (Huazhong University of Science and Technology) Ministry of Education China

Rolling bearing faults are among the primary causes of breakdown in mechanical equipment. Aiming at the vibration signals of rolling bearing which are non-stationary and easy to be disturbed by noise, a novel fault diagnosis method based on curvelet transform and metric learning is proposed. This method consists of 3 parts. The first one is feature engineering which includes reshaping the original timing features of rolling bearings, employing curvelet transform to transform reshaped features and making its coefficients as the new features. Curvelet transform can analyse the original signal from many angles. The second one is employing metric learning to map these new features into special embedding space. The last one is applying KNN classifier to detect the rolling bearing faults. Metric learning can effectively improve the performance of KNN by learning a mapping matrix to modify the distribution of samples. The proposed method overcomes the problems such as the subjectivity and blindness of manual feature extraction, poor coupling in each stage and sensitive to the effect of noise. Extensive simulations based on several data-sets show that the our method has better performance on bearing fault diagnosis than traditional methods.

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A high-order disturbance observer based sliding mode velocity control of mobile wheeled inverted pendulum systems

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A high-order disturbance observer based sliding mode velocit...

World Congress on Intelligent control and Automation (WCICA)

作者： Songhyok Ri Jian Huang Chunjing Tao Myonghyok Ri Yongsok Ri Daeho Han School of Automation Huazhong University of Science and Technology Wuhan CHINA Department of Control Science University of Sciences Pyongyang D.P.R. of Korea Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan CHINA R&D department National Research Center for Rehabilitation Technical Aids Beijing Automation school Kim Chaek University of Technology Pyongyang D.P.R. of Korea Department of Phisics University of Sciences Pyongyang D.P.R. of Korea

ISBN: (纸本)9781467384155

In this paper, a novel disturbance observer (DO) for the Mobile Wheeled Inverted Pendulum (MWIP) system is proposed. A choice method of optimal gain matrices is also proposed for a given robust gain, which can improve the estimation precision of the DO. Combining the proposed DO and Sliding Mode control (SMC), a new sliding mode velocity control method is designed for the MWIP system. The convergency of the DO is proved by Lyapunov theorem. And the stability of the closed-loop system is achieved through the appropriate selection of sliding surface coefficients. The effectiveness of all proposed methods is verified by simulation results for the MWIP system.

关键词： Wheels Automation Velocity control Uncertainty Observers Robustness

End-to-end 2D-3D Registration between image and LiDAR Point Cloud for Vehicle Localization

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arXiv 2023年

作者： Wang, Guangming Zheng, Yu Guo, Yanfeng Liu, Zhe Zhu, Yixiang Burgard, Wolfram Wang, Hesheng Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China China Department of Artificial Intelligence Shanghai Jiao Tong University Shanghai200240 China Electrical and Computer Engineering University of California Los Angeles United States Institute of Artificial Intelligence Shanghai Jiao Tong University Shanghai200240 China Department of Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Engineering University of Technology Nuremberg Germany

Robot localization using a previously built map is essential for a variety of tasks including highly accurate navigation and mobile manipulation. A popular approach to robot localization is based on image-to-point cloud registration, which combines illumination-invariant LiDAR-based mapping with economical image-based localization. However, the recent works for image-to-point cloud registration either divide the registration into separate modules or project the point cloud to the depth image to register the RGB and depth images. In this paper, we present I2PNet, a novel end-to-end 2D-3D registration network. I2PNet directly registers the raw 3D point cloud with the 2D RGB image using differential modules with a unique target. The 2D-3D cost volume module for differential 2D-3D association is proposed to bridge feature extraction and pose regression. 2D-3D cost volume module implicitly constructs the soft point-to-pixel correspondence on the intrinsic-independent normalized plane of the pinhole camera model. Moreover, we introduce an outlier mask prediction module to filter the outliers in the 2D-3D association before pose regression. Furthermore, we propose the coarse-to-fine 2D-3D registration architecture to increase localization accuracy. We conduct extensive localization experiments on the KITTI Odometry and nuScenes datasets. The results demonstrate that I2PNet outperforms the state-of-the-art by a large margin. In addition, I2PNet has a higher efficiency than the previous works and can perform the localization in real-time. Moreover, we extend the application of I2PNet to the camera-LiDAR online calibration and demonstrate that I2PNet outperforms recent approaches on the online calibration task. Copyright © 2023, The Authors. All rights reserved.

关键词： Calibration