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检索条件"机构=Geometry Robotics and the School of Computer Science and Technology"

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Model LEGO: Creating Models Like Disassembling and Assembling Building Blocks 38

Model LEGO: Creating Models Like Disassembling and Assemblin...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Hu, Jiacong Gao, Jing Ye, Jingwen Gao, Yang Wang, Xingen Feng, Zunlei Song, Mingli College of Computer Science and Technology Zhejiang University China Robotics Institute School of Computer Science Carnegie Mellon University United States Electrical and Computer Engineering National University of Singapore Singapore School of Software Technology Zhejiang University China State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China Bangsheng Technology Co. Ltd. China

With the rapid development of deep learning, the increasing complexity and scale of parameters make training a new model increasingly resource-intensive. In this paper, we start from the classic convolutional neural network (CNN) and explore a paradigm that does not require training to obtain new models. Similar to the birth of CNN inspired by receptive fields in the biological visual system, we draw inspiration from the information subsystem pathways in the biological visual system and propose Model Disassembling and Assembling (MDA). During model disassembling, we introduce the concept of relative contribution and propose a component locating technique to extract task-aware components from trained CNN classifiers. For model assembling, we present the alignment padding strategy and parameter scaling strategy to construct a new model tailored for a specific task, utilizing the disassembled task-aware components. The entire process is akin to playing with LEGO bricks, enabling arbitrary assembly of new models, and providing a novel perspective for model creation and reuse. Extensive experiments showcase that task-aware components disassembled from CNN classifiers or new models assembled using these components closely match or even surpass the performance of the baseline, demonstrating its promising results for model reuse. Furthermore, MDA exhibits diverse potential applications, with comprehensive experiments exploring model decision route analysis, model compression, knowledge distillation, and more. For more information, please visit https://***/. © 2024 Neural information processing systems foundation. All rights reserved.

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Edge-SAN: An Edge-Prompted Foundation Model for Accurate Nuclei Instance Segmentation in Histology Images

Edge-SAN: An Edge-Prompted Foundation Model for Accurate Nuc...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wu, Xuening Shen, Yiqing Zhao, Qing Kang, Yanlan Hu, Ruiqi Zhang, Wenqiang Fudan University Shanghai Engineering Research Center of Ai & Robotics Academy for Engineering & Technology Shanghai China Johns Hopkins University Department of Computer Science BaltimoreMD United States Hong Kong Polytechnic University Department of Applied Mathematics Hong Kong Fudan University Engineering Research Center of Ai & Robotics Ministry of Education Academy for Engineering & Technology School of Computer Science Shanghai China

ISBN: (纸本)9798350386226

Accurate nuclei segmentation is fundamental in histology image analysis, playing an essential role in cancer grading and diagnosis. However, this task remains challenging due to variations in staining protocols, heterogeneity among nuclei types, and the densely clustered nature of nuclei. While SAM exhibits zero-shot generalization capabilities in natural image segmentation, its performance degrades when applied to nuclei segmentation in histology images. Existing adaptations of SAM for medical imaging primarily focus on organ or lesion segmentation, which differs substantially from nuclei segmentation due to the unique characteristics of nuclei - specifically, their sparse distribution combined with dense clustering. To address these challenges, we propose Edge-SAN (Segment Any Nuclei with Edge Prompting), an interactive segmentation foundation model specifically designed for nuclei segmentation. Edge-SAN introduces a novel edge prompting method that enhances the delineation of nuclei boundaries, particularly among densely clustered nuclei, by leveraging edge information to improve segmentation accuracy. We evaluate Edge-SAN on 12 diverse datasets in both few-shot and zero-shot scenarios, demonstrating its effectiveness as a foundation model for nuclei segmentation, achieving 66.81% AJI and 73.13% DSC - improvements of 16.33% and 15.48% over SAM-Med2D, respectively. The code is available at https://***/deep-geo/Edge-SAN. © 2024 IEEE.

关键词： Deep Learning Digital Pathology Foundation Model Nuclei Segmentation Segment Anything Model

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TagOOD: A Novel Approach to Out-of-Distribution Detection via Vision-Language Representations and Class Center Learning 24

TagOOD: A Novel Approach to Out-of-Distribution Detection vi...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Li, Jinglun Zhou, Xinyu Jiang, Kaixun Hong, Lingyi Guo, Pinxue Chen, Zhaoyu Ge, Weifeng Zhang, Wenqiang Shanghai Engineering Research Center of Ai & Robotics Academy for Engineering & Technology Fudan University China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China Engineering Research Center of Ai & Robotics Ministry of Education Academy for Engineering & Technology Fudan University China

ISBN: (纸本)9798400706868

Multimodal fusion, leveraging data like vision and language, is rapidly gaining traction. This enriched data representation improves performance across various tasks. Existing methods for out-of-distribution (OOD) detection, a critical area where AI models encounter unseen data in real-world scenarios, rely heavily on whole-image features. These image-level features can include irrelevant information that hinders the detection of OOD samples, ultimately limiting overall performance. In this paper, we propose TagOOD, a novel approach for OOD detection that leverages vision language representations to achieve label-free object feature decoupling from whole images. This decomposition enables a more focused analysis of object semantics, enhancing OOD detection performance. Subsequently, TagOOD trains a lightweight network on the extracted object features to learn representative class centers. These centers capture the central tendencies of IND object classes, minimizing the influence of irrelevant image features during OOD detection. Finally, our approach efficiently detects OOD samples by calculating distance-based metrics as OOD scores between learned centers and test samples. We conduct extensive experiments to evaluate TagOOD on several benchmark datasets and demonstrate its superior performance compared to existing OOD detection methods. This work presents a novel perspective for further exploration of multimodal information utilization in OOD detection, with potential applications across various tasks. Code is available at: https://***/Jarvisgivemeasuit/tagood. © 2024 ACM.

关键词： Semantics

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Feature Extraction Network SFPN for Edge Computing Scenarios 23

Feature Extraction Network SFPN for Edge Computing Scenarios

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6th International Conference on Signal Processing and Machine Learning, SPML 2023

作者： Yao, Minda Liu, Jueting Liu, Xingyu Chen, Wei Xu, Tingting Liu, Yingchun School of Computer Science & Technology China University of Mining and Technology Xuzhou221116 China Beijing100083 China Key Laboratory of Intelligent Mining and Robotics Ministry of Emergency Management China

ISBN: (纸本)9798400707575

Aiming at the problems that the existing networks do not adapt to the image characteristics of edge scenes, do not effectively process the detail information in images and have insufficient feature fusion, this thesis proposes a feature extraction network model SFPN that can better utilize the different depths and detail semantic information of the network. Firstly, the S-soft unit is designed to process the detail information of the feature map. Secondly, this thesis proposes a new secondary fusion strategy that combines the features of the middle layer are fused with the lower and higher layers respectively, and with this, SFPN Block is proposed as an auxiliary classifier, which participates in the final prediction by weighting, fully considering the prediction information of different layers of the network. Finally, a joint loss function is introduced for training, so that the feature extraction ability of the network for targets with large scale changes and small sizes is improved, and it is more adapted to the edge computing scenario of detection requirements and improve the accuracy of feature extraction. © 2023 ACM.

关键词： Feature extraction

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Transition in Focus of Prediction Tasks for Skeleton Graph Component Detection with Transformer 6

Transition in Focus of Prediction Tasks for Skeleton Graph C...

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6th ACM International Conference on Multimedia in Asia, MMAsia 2024

作者： Wang, Zhiyuan Yang, Cong Zhang, Yulu Boukhers, Zeyd Sui, Wei Ji, Yi Liu, Chunping School of Computer Science and Techonology Soochow University Suzhou China School of Future Science and Engineering Soochow University Suzhou China Chery Hangzhou China Fraunhofer Institute for Applied Information Technology FIT Sankt Augustin Germany Horizon Robotics Beijing China

ISBN: (纸本)9798400712739

Recent advancements in skeleton extraction have significantly improved the process by simplifying the skeleton regression task into graph component detection. Despite the advancements in skeleton topology, accuracy in detailing skeletal parts remains challenging, with specific issues such as jagged edges in high-resolution images. This paper identifies the limitations of current detection models that can adapt during the decomposition and reconstruction phases, which impacts the overall precision of the extraction. In response, we propose an approach that revises the primary focus of the detection tasks. Inspired by the success of pixel-wise binary classification methods, we propose a gradual transition in focus from a coordinate localization regression task to a classification task of predicting points during the training process. This transition can be achieved by adjusting the number of object queries in the Transformer model. Theoretical and experimental evaluations validate the effectiveness of our approach. Our method yields significant improvements in performance over the baseline across various shape and image datasets (e.g., 0.836 vs. 0.826 for BlumNet on the SK1491 dataset). © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Musculoskeletal system

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Semantic Traffic Element Removal by Learning to Follow Linguistic Instructions

Semantic Traffic Element Removal by Learning to Follow Lingu...

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2023 IEEE International Conference on Unmanned Systems, ICUS 2023

作者： Feng, Xiaodan Su, Yuanqi Liu, Yuehu Zhang, Chi School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Xi'an China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Shaanxi Xi'an China

ISBN: (纸本)9798350316308

We propose a novel task of semantic object removal in traffic scenes through human-guided image editing. Unlike traditional methods, our model does not require image masks;instead, it effectively removes semantic elements from a traffic scene image based solely on an input image and linguistic instruction. To achieve this objective, we use large-scale pre-trained models to construct a traffic scene image dataset suitable for human-guided image editing. We train a text-guided image translation model on this dataset. During inference, our model quickly generalizes to photorealistic traffic scene images and user-written instructions, obviating the need for fine-tuning and enabling rapid removal of semantic object elements from images within seconds. Experimental results demonstrate that our model achieves satisfying removal performance in the traffic scene semantic object removal task, showing the potential advantages of text guidance in image processing tasks. © 2023 IEEE.

关键词： Semantics

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SYNLOCO: Synthesizing Central Pattern Generator and Reinforcement Learning for Quadruped Locomotion 63

SYNLOCO: Synthesizing Central Pattern Generator and Reinforc...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Zhang, Xinyu Xiao, Zhiyuan Zhang, Qingrui Pan, Wei Shenzhen Campus of Sun Yatsen University School of Aeronautics and Astronautics Shenzhen518107 China The University of Manchester Department of Computer Science United Kingdom Delft University of Technology Department of Cognitive Robotics Netherlands

ISBN: (纸本)9798350316339

The Central Pattern Generator (CPG) is adept at generating rhythmic gait patterns characterized by consistent timing and adequate foot clearance. Yet, its open-loop configuration often fails to adjust the system's control performance in response to environmental variations. On the other hand, Reinforcement Learning (RL), celebrated for its model-free properties, has gained significant traction in robotics due to its inherent adaptability and robustness. However, initiating traditional RL approaches from the ground up presents a risk of converging to suboptimal local minima and slow learning convergence. In this paper, we propose a quadruped locomotion framework-called SYNLOCO-by synthesizing CPG and RL, which can ingeniously integrate the strengths of both methods, enabling the development of a locomotion controller that is both stable and natural with partial state observations (e.g., no velocity measurements). To optimize the learning trajectory of SYNLOCO, a two-phase training strategy is presented. Both ablation analysis and experimental comparison are performed using a real quadruped robot under varied conditions, including distinct velocities, terrains, and payload capacities. The experiments showcase SYNLOCO's efficiency in producing consistent and clear-footed gaits across diverse scenarios, despite no velocity measurements. The developed controller exhibits resilience against substantial parameter variations, underscoring its potential for robust real-world applications. © 2024 IEEE.

关键词： Reinforcement learning

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A Comparative Study of Lossless Compression Technique in Quantum Secure Direct Communication 1

A Comparative Study of Lossless Compression Technique in Qua...

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1st International Conference on Cyber Security and Computing, CyberComp 2024

作者： Ishak, Izlin Zuriani Wooi, Liew Seow Harun, Nur Ziadah Azahari, Nur Shahirah Ramli, Sofia Najwa Ghazali, Osman Politeknik Seberang Perai Department of Information Technology and Communication Pulau Pinang Malaysia Universiti Putra Malaysia Faculty of Computer Science and Information Technology Department of Computer Science Selangor Malaysia Raffles University Faculty of AI and Robotics Johor Malaysia Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Department of Information Security and Web Technology Johor Malaysia School of Computing Universiti Utara Malaysia Kedah Malaysia

ISBN: (纸本)9798350387728

This is a comparative study to explore the performance of three lossless compression techniques, namely Run Length Encoding (RLE), Shannon-Fano Coding (SFC), and Huffman Coding (HC) in the context of a Hybrid M-Ary in Braided Single Stage (HMBSS) protocol communication scheme. The previous study on HMBSS using HC found that HC's compression technique has drawbacks, i.e., it is sensitive to errors and noise in the transmission channel and limited to certain data types. These drawbacks did affect the value of the compression rate limit. In this study, we evaluate the effectiveness of RLE, SFC, and HC in terms of data size reduction, coding efficiency, and data transmission time by comparing their results arising from the simulation experiments using Python coding in Jupiter Notebook and developed based on the HMBSS protocol algorithm. This simulation experiment starts with an initial six randomly generated datasets. Each dataset is tested using three compression techniques, which resulted in 18 different datasets. The results from the experiments reveal that SFC and HC consistently achieved a higher compression ratio and coding efficiency than RLE. These two compression techniques' algorithms effectively reduce the data size while preserving the original information. In our study, we also find that the data transmission time is increased with larger text sizes for all three compression techniques. This indicates that the larger the data size, the more transmission time is required with regard to what the compression technique's algorithm is. © 2024 IEEE.

关键词： Quantum communication

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Research on natural environment protection based on blockchain technology to facilitate ecological balance and regional revitalization 4

Research on natural environment protection based on blockcha...

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4th International Conference on Industrial IoT, Big Data and Supply Chain, IIoTBDSC 2023

作者： Wan, Chenye Fu, Taoran Wang, Haochen Wang, Xing Luo, Junyou Wang, Ziqian School of Information Science and Technology Nantong University Nantong China School of Computer and Communication Hunan Institute of Engineering Xiangtan China School of Computational Science and Electronics Hunan Institute of Engineering Xiangtan China School of Robotics Hunan University Changsha China

ISBN: (纸本)9798350341690

Now the Masai Mara population continues to expand, the wildlife in the reserve is facing serious threats to their survival. The purpose of this report is to establish a management strategy model for local protected areas that balances the mutual interests between local people and wildlife in ***, Masai Mara divides the protected area into three parts, planting area, protected area, and livestock area, and then conducts correlation analysis on different areas, and concludes that the reduction in the number of wild animals is closely related to the degree of population density, and the area of cultivated land and forest area is highly correlated. So a nonlinear programming model of multiple strategies is established to plan the planting area. Then establish a comprehensive evaluation model based on the entropy weight method, quantify various strategies, and use gray correlation prediction to obtain solutions. This article introduces blockchain related technologies and uses its decentralization, security and transparency to build an architecture that contributes to the actual government protection of wild animals. © 2023 IEEE.

关键词： Conservation

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Fundus Image Classification via an Integrated Deep Learning Model and Random Forest for Glaucoma Diagnostics 2nd

Fundus Image Classification via an Integrated Deep Learnin...

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2nd International Conference on Intelligent Manufacturing and robotics, ICIMR 2024

作者： Zeng, Haotian Cong, Jiheng Hong, Hantong Yu, Zihan Zhang, Fan Li, Guojie School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China School of Robotics Xi’an Jiaotong-Liverpool University Suzhou China Hebei University of Environmental Engineering Qinhuangdao China School of Electrical Engineering Electronics and Computer Science University of Liverpool Liverpool United Kingdom

ISBN: (纸本)9789819639489

Glaucoma is a leading cause of irreversible blindness, often worsened by delayed diagnosis. Traditional diagnostic methods have limitations in early detection and require significant expertise. Recent advancements in machine learning and deep learning have shown promise in enhancing the diagnostic accuracy and efficiency of glaucoma detection using fundus imagery. This study presents a four-stage method for glaucoma classification. Fundus images are processed using VGG16 and ViT for feature extraction, capturing both local and global features. These features are then fused and reduced in dimensionality using Principal Component Analysis. Finally, the reduced features are classified using a Random Forest classifier. The integrated feature fusion model demonstrates significant improvements in diagnostic performance, achieving higher accuracy, specificity, and sensitivity in distinguishing between non-referable and referable glaucoma classes compared to traditional methods. Specifically, our model achieved an accuracy of 94.2%, an F1 score of 94.2%, a sensitivity of 94.44%, and a specificity of 94.0%. The use of both CNNs and ViTs for feature extraction leverages their strengths, resulting in a more effective diagnostic tool. The combination of CNNs, ViTs, and Random Forest classifiers, along with advanced data augmentation techniques, shows substantial potential for early glaucoma detection and ongoing monitoring. This approach addresses the limitations of current machine learning models and enhances diagnostic accuracy and efficiency, making it a promising tool for clinical settings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ophthalmology

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