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DeepBranchTracer: A Generally-Applicable Approach to Curvilinear Structure Reconstruction Using Multi-Feature Learning

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arXiv 2024年

作者： Liu, Chao Zhao, Ting Zheng, Nenggan Qiushi Academy for Advanced Studies Zhejiang University Zhejiang Hangzhou China College of Computer Science and Techology Zhejiang University Zhejiang Hangzhou China Independent Researcher State Key Lab of Brain-Machine Intelligence Zhejiang University Zhejiang Hangzhou China Zhejiang Hangzhou China

Curvilinear structures, which include line-like continuous objects, are fundamental geometrical elements in image-based applications. Reconstructing these structures from images constitutes a pivotal research area in computer vision. However, the complex topology and ambiguous image evidence render this process a challenging task. In this paper, we introduce DeepBranchTracer, a novel method that learns both external image features and internal geometric characteristics to reconstruct curvilinear structures. Firstly, we formulate the curvilinear structures extraction as a geometric attribute estimation problem. Then, a curvilinear structure feature learning network is designed to extract essential branch attributes, including the image features of centerline and boundary, and the geometric features of direction and radius. Finally, utilizing a multi-feature fusion tracing strategy, our model iteratively traces the entire branch by integrating the extracted image and geometric features. We extensively evaluated our model on both 2D and 3D datasets, demonstrating its superior performance over existing segmentation and reconstruction methods in terms of accuracy and continuity. Copyright © 2024, The Authors. All rights reserved.

关键词： Geometry

A Silk-Based Multifunctional and Bidirectional Neural Interface 38

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A Silk-Based Multifunctional and Bidirectional Neural Interf...

38th IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2025

作者： Wang, Xiner Wang, Yan Liu, Yuxin Wei, Xiaoling Sun, Liuyang Tao, Tiger H. Zhou, Zhitao 2020 X-Lab Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China School of Graduate Study University of Chinese Academy of Sciences Beijing China College of Chemistry and Materials Science Shanghai Normal University Shanghai China Neuroxess Co. Ltd Shanghai China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China Guangdong Institute of Intelligence Science and Technology Zhuhai Guangdong Hengqin China Tianqiao and Chrissy Chen Institute for Translational Research Shanghai China

ISBN: (纸本)9798331508890

We present the development of a silk-based multifunctional and bidirectional neural interface that integrates neural recording, temperature sensing, intracranial pressure (ICP) monitoring, electrical stimulation, and photothermal stimulation. Owing to the excellent mechanical properties of silk fibroins, this neural interface not only conforms seamlessly to the brain surface but also demonstrates robust mechanical stability. Moreover, the ease of functionalizing silk materials enables the interface to achieve exceptional temperature and pressure sensing capabilities, along with significant photothermal effects. This multifunctional interface can be incorporated into brain-machine interfaces (BMIs) to investigate and modulate neural activities and intracranial physiological states. Our innovation introduces a novel, flexible neural interface for multimodal recording and modulation, expanding opportunities for advanced brain research and offering potential applications in disease diagnostics and therapeutics. © 2025 IEEE.

关键词： Silk

Cross-Domain Animal Pose Estimation with Skeleton Anomaly-Aware Learning

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Han, Le Chen, Kaixuan Zhao, Lei Jiang, Yangbo Wang, Pengfei Zheng, Nenggan Zhejiang Hangzhou310007 China Zhejiang University College of Computer Science and Technology Zhejiang Hangzhou310007 China Zhejiang University State Key Laboratory of Blockchain and Data Security Zhejiang Hangzhou310007 China Institute of Blockchain and Data Security. China Zhejiang University School of Software Technology Ningbo China Zhejiang University State Key Lab of Brain-Machine Intelligence Hangzhou310007 China Zhejiang Provincial Government ZJU Collaborative Innovation Center for Artificial Intelligence by MOE Hangzhou310007 China Bengbu University School of Computer and Information Engineering Bengbu233030 China

Animal pose estimation is often constrained by the scarcity of annotations and the diversity of scenarios and species. The pseudo-label generation based unsupervised domain adaptation paradigm, which discriminates the predicted keypoints of unlabeled data based on the skeleton position consistency, has demonstrated effectiveness for such problems. However, existing methods generate pseudo-labels with massive false positives, because they cannot effectively distinguish sample pairs with the same errors. In this study, we propose a cross-domain animal pose estimation model from a novel perspective of skeleton anomaly learning. We construct a graph contrastive learning mechanism to acquire the skeleton anomaly-aware knowledge, which enables the generation of accurate pseudo-labels for target domain and imposes graph constraint on unlabeled data. And a skeleton anomaly-feedback based domain adaptation framework is designed to facilitate implicit alignment of object-specific features and joint training of cross-domain. Besides, we propose a novel rat pose dataset named UDARP-9.4K to address the gap of small-sized animal pose datasets encompassing diverse experimental scenarios. The related datasets are reviewed and evaluated in detail. Extensive experiments are conducted on UDARP-9.4K and two public datasets to demonstrate the superiority of the proposed model in cross-scenarios and cross-species animal pose estimation tasks. Further analysis reveals the effectiveness of the proposed model for skeleton structure feature learning. The UDARP-9.4K dataset is available here. © 1991-2012 IEEE.

关键词： Invertebrates

Generalizable Sleep Staging via Multi-Level Domain Alignment

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arXiv 2023年

作者： Wang, Jiquan Zhao, Sha Jiang, Haiteng Li, Shijian Li, Tao Pan, Gang State Key Laboratory of Brain-machine Intelligence Zhejiang University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Department of Neurobiology Affiliated Mental Health Center Hangzhou Seventh People’s Hospital Zhejiang University School of Medicine Hangzhou China MOE Frontier Science Center for Brain Science and Brain-machine Integration Zhejiang University Hangzhou China

Automatic sleep staging is essential for sleep assessment and disorder diagnosis. Most existing methods depend on one specific dataset and are limited to be generalized to other unseen datasets, for which the training data and testing data are from the same dataset. In this paper, we introduce domain generalization into automatic sleep staging and propose the task of generalizable sleep staging which aims to improve the model generalization ability to unseen datasets. Inspired by existing domain generalization methods, we adopt the feature alignment idea and propose a framework called SleepDG to solve it. Considering both of local salient features and sequential features are important for sleep staging, we propose a Multi-level Feature Alignment combining epoch-level and sequence-level feature alignment to learn domain-invariant feature representations. Specifically, we design an Epoch-level Feature Alignment to align the feature distribution of each single sleep epoch among different domains, and a Sequence-level Feature Alignment to minimize the discrepancy of sequential features among different domains. SleepDG is validated on five public datasets, achieving the state-of-the-art performance. © 2023, CC BY-SA.

关键词： Sleep research

MCA: Moment Channel Attention Networks

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arXiv 2024年

作者： Jiang, Yangbo Jiang, Zhiwei Han, Le Huang, Zenan Zheng, Nenggan Qiushi Academy for Advanced Studies Zhejiang University Zhejiang Hangzhou China College of Computer Science and Technology Zhejiang University Zhejiang Hangzhou China Guangzhou Electronic Technology Co. Ltd. Chinese Academy of Sciences Guangzhou China State Key Lab of Brain-Machine Intelligence Zhejiang University Zhejiang Hangzhou China Zhejiang Hangzhou China

Channel attention mechanisms endeavor to recalibrate channel weights to enhance representation abilities of networks. However, mainstream methods often rely solely on global average pooling as the feature squeezer, which significantly limits the overall potential of models. In this paper, we investigate the statistical moments of feature maps within a neural network. Our findings highlight the critical role of high-order moments in enhancing model capacity. Consequently, we introduce a flexible and comprehensive mechanism termed Extensive Moment Aggregation (EMA) to capture the global spatial context. Building upon this mechanism, we propose the Moment Channel Attention (MCA) framework, which efficiently incorporates multiple levels of moment-based information while minimizing additional computation costs through our Cross Moment Convolution (CMC) module. The CMC module via channel-wise convolution layer to capture multiple order moment information as well as cross channel features. The MCA block is designed to be lightweight and easily integrated into a variety of neural network architectures. Experimental results on classical image classification, object detection, and instance segmentation tasks demonstrate that our proposed method achieves state-of-the-art results, outperforming existing channel attention methods. © 2024, CC BY-NC-SA.

关键词： Convolution

DANI-Net: Uncalibrated Photometric Stereo by Differentiable Shadow Handling, Anisotropic Reflectance Modeling, and Neural Inverse Rendering

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DANI-Net: Uncalibrated Photometric Stereo by Differentiable ...

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zongrui Li Qian Zheng Boxin Shi Gang Pan Xudong Jiang School of Electrical and Electronic Engineering Nanyang Technological University Singapore The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing China National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing China

Uncalibrated photometric stereo (UPS) is challenging due to the inherent ambiguity brought by the unknown light. Although the ambiguity is alleviated on non-Lambertian objects, the problem is still difficult to solve for more general objects with complex shapes introducing irregular shadows and general materials with complex reflectance like anisotropic reflectance. To exploit cues from shadow and reflectance to solve UPS and improve performance on general materials, we propose DANI-Net, an inverse rendering framework with differentiable shadow handling and anisotropic reflectance modeling. Unlike most previous methods that use non-differentiable shadow maps and assume isotropic material, our network benefits from cues of shadow and anisotropic reflectance through two differentiable paths. Experiments on multiple real-world datasets demonstrate our superior and robust performance.

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DANI-Net: Uncalibrated Photometric Stereo by Differentiable Shadow Handling, Anisotropic Reflectance Modeling, and Neural Inverse Rendering

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arXiv 2023年

作者： Li, Zongrui Zheng, Qian Shi, Boxin Pan, Gang Jiang, Xudong School of Electrical and Electronic Engineering Nanyang Technological University Singapore The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing China National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing China

关键词： Reflection

SLC2-SLAM: Semantic-guided Loop Closure using Shared Latent Code for NeRF SLAM

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arXiv 2025年

作者： Ming, Yuhang Ma, Di Dai, Weichen Yang, Han Fan, Rui Zhang, Guofeng Kong, Wanzeng School of Computer Science Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province Hangzhou Dianzi University Hangzhou310018 China College of Electronics & Information Engineering Shanghai Institute of Intelligent Science and Technology Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China State Key Lab of CAD&CG Zhejiang University Hangzhou310058 China

Targeting the notorious cumulative drift errors in NeRF SLAM, we propose a Semantic-guided Loop Closure using Shared Latent Code, dubbed SLC2-SLAM. We argue that latent codes stored in many NeRF SLAM systems are not fully exploited, as they are only used for better reconstruction. In this paper, we propose a simple yet effective way to detect potential loops using the same latent codes as local features. To further improve the loop detection performance, we use the semantic information, which are also decoded from the same latent codes to guide the aggregation of local features. Finally, with the potential loops detected, we close them with a graph optimization followed by bundle adjustment to refine both the estimated poses and the reconstructed scene. To evaluate the performance of our SLC2-SLAM, we conduct extensive experiments on Replica and ScanNet datasets. Our proposed semantic-guided loop closure significantly outperforms the pre-trained NetVLAD and ORB combined with Bag-of-Words, which are used in all the other NeRF SLAM with loop closure. As a result, our SLC2-SLAM also demonstrated better tracking and reconstruction performance, especially in larger scenes with more loops, like ScanNet. Copyright © 2025, The Authors. All rights reserved.

关键词： SLAM robotics

Emergence and reconfiguration of modular structure for synaptic neural networks during continual familiarity detection

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arXiv 2023年

作者： Gu, Shi Mattar, Marcelo G. Tang, Huajin Pan, Gang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China Psychology Department New York University United States College of Computer Science and Technology Zhejiang University Hangzhou China State Key Laboratory of Brain Machine Intelligence Zhejiang University Hangzhou China

While advances in artificial intelligence and neuroscience have enabled the emergence of neural networks capable of learning a wide variety of tasks, our understanding of the temporal dynamics of these networks remains limited. Here, we study the temporal dynamics during learning of Hebbian Feedforward (HebbFF) neural networks in tasks of continual familiarity detection. Drawing inspiration from the field of network neuroscience, we examine the network's dynamic reconfiguration, focusing on how network modules evolve throughout learning. Through a comprehensive assessment involving metrics like network accuracy, modular flexibility, and distribution entropy across diverse learning modes, our approach reveals various previously unknown patterns of network reconfiguration. In particular, we find that the emergence of network modularity is a salient predictor of performance, and that modularization strengthens with increasing flexibility throughout learning. These insights not only elucidate the nuanced interplay of network modularity, accuracy, and learning dynamics but also bridge our understanding of learning in artificial and biological realms. © 2023, CC BY.

关键词： Feedforward neural networks