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1. CNN Mixture-of-Depths NSTL国家科技图书文献中心

Rinor Cakaj |  Jens Mehnert... -  《Computer Vision - ACCV 2024,Part VII》 -  Asian Conference on Computer Vision - 2025, - 148~166 - 共19页

摘要: that require dynamic computation graphs, CNN MoD uses | -tuning. CNN MoD either matches the performance of | We introduce Mixture-of-Depths (MoD) for |  Convolutional Neural Networks (CNNs), a novel approach that |  enhances the computational efficiency of CNNs by
关键词: CNN |  Mixture-of-Depths |  Computational efficiency |  Inference speed

2. Lightweight Single Image Super-Resolution Network Integrating CNN and Transformer NSTL国家科技图书文献中心

Kai Zhu |  Li Chen -  《Pattern Recognition,Part XXXII》 -  International Conference on Pattern Recognition - 2025, - 212~225 - 共14页

摘要: based on CNN and Transformer architectures. However |  an Alternating CNN Transformer Block and an |  Integrative CNN Efficient Transformer for single image super |  combining CNN within and between Transformer modules. In |  Gated CNN and Parallel Channel Attention, aiming to
关键词: SISR |  CNN |  Transformer |  Attention |  Deep learning

3. Rolling bearing fault diagnosis based on VMD-CNN-BiLSTM NSTL国家科技图书文献中心

Zhiqiang Wu -  《Fourth International Conference on Computer Vision,Application,and Algorithm (CVAA 2024)》 -  International Conference on Computer Vision,Application,and Algorithm - 2025, - 1348622.1~1348622.7 - 共7页

摘要:), Convolutional Neural Network (CNN), and Bidirectional Long | -CNN-BiLSTM, for rolling bearing fault diagnosis. VMD |  endpoint effects. The CNN layer extracts local features |  innovation lies in integrating VMD with CNN and BiLSTM |  demonstrate that the VMD-CNN-BiLSTM model achieves a
关键词: Rolling bearings |  Fault diagnosis |  VMD |  CNN |  BiLSTM

4. PolSAR Image Classification Using Superpixel Profile and CNN NSTL国家科技图书文献中心

Nabajyoti Das |  Swarnajyoti Patra... -  《Pattern Recognition,Part II》 -  International Conference on Pattern Recognition - 2025, - 320~334 - 共15页

摘要: (CNN) based frameworks are being applied to |  performance of CNN for PolSAR image classification is |  features. Although CNN automatically extracts abstract |  into a CNN model for classification. The experiment |  comparison to the state-of-the-art CNN model.
关键词: PolSAR images |  Superpixel profile |  Convolutional neural network (CNN)

5. FARSE-CNN: Fully Asynchronous, Recurrent and Sparse Event-Based CNN NSTL国家科技图书文献中心

Riccardo Santambrogi... |  Marco Cannici... -  《Computer Vision - ECCV 2024,Part LIV》 -  European Conference on Computer Vision - 2025, - 1~18 - 共18页

摘要: Asynchronous, Recurrent and Sparse Event-Based CNN (FARSE-CNN |  recognition. FARSE-CNN achieves similar or better |  released at https://github.com/AIRLab-POLIMI/farse-cnn. | Event cameras are neuromorphic image sensors |  that respond to per-pixel brightness changes
关键词: Event cameras |  Deep learning architectures

6. Application of CNN in condition monitoring of high voltage switchgear NSTL国家科技图书文献中心

Lei Sun |  Yao Li... -  《International Conference on Physics,Photonics,and Optical Engineering (ICPPOE 2024),Part One of Two Parts》 -  International Conference on Physics,Photonics,and Optical Engineering - 2025, - 135521B.1~135521B.7 - 共7页

摘要: of Convolutional Neural Networks (CNN) in high |  distribution monitoring. Specifically tailored CNN |  distribution monitoring, presenting CNN as a promising |  involve continuous refinement of CNN architectures and | Study explores the transformative application
关键词: Convolutional neural networks (CNN) |  High-voltage switchgear |  State monitoring |  Power distribution |  Precision |  Efficiency |  Fault detection

7. Diagnosing ADHD with 1D-CNN: Efficient Analysis of fMRI Data NSTL国家科技图书文献中心

Qurat Ul Ain |  Soyiba Jawed -  《Seventeenth International Conference on Machine Vision (ICMV 2024)》 -  International Conference on Machine Vision - 2025, - 135170E.1~135170E.8 - 共8页

摘要: Convolution Neural Network (1D-CNN) for the diagnosis of |  Independent Component Analysis and then passed to 1D-CNN for | Attention Deficit Hyperactivity Disorder (ADHD | ) is a type of neurodevelopmental disease affecting |  the mental health of children and adults. So
关键词: ADHD |  Deep learning |  ICA |  1D-CNN |  Rs-fMRI

8. Towards a Lightweight CNN for Semantic Food Segmentation NSTL国家科技图书文献中心

Bastian Munoz |  Beatriz Remeseiro... -  《Progress in Pattern Recognition,Image Analysis,Computer Vision,and Applications,Part I》 -  Iberoamerican Congress on Pattern Recognition - 2025, - 1~15 - 共15页

摘要: lightweight CNN EfficientNet-B1 and the Atrous Spatial | Semantic food segmentation is an important |  task for the development of nutritional systems that |  effectively manage daily diets. Recent advances in semantic |  segmentation have brought great performance improvements
关键词: Deep network |  Lightweights CNN |  DeepLabv3+ |  Semantic food segmentation

9. Block Cipher Algorithm Identification Based on CNN-Transformer Fusion Model NSTL国家科技图书文献中心

Rongna Xie |  Xiaoyu Chen... -  《Pattern Recognition and Computer Vision,Part XI》 -  Chinese Conference on Pattern Recognition and Computer Vision - 2025, - 97~110 - 共14页

摘要: features. The model is based on CNN-Transformer, which | Cryptanalysis is predicated on the recognition |  of cipher algorithms, but in practice, researcher |  often do not know the cipher algorithm used. This |  paper focuses on block cipher algorithms
关键词: Block cipher algorithms identification |  Interpretable model |  CNN-transformer fusion

10. On the Influence of CNN-Based Feature Learning Modules in Neural Speaker Verification Framework NSTL国家科技图书文献中心

Jahangir Alam |  Md Shahidul Alam -  《Speech and Computer,Part II》 -  International Conference on Speech and Computer - 2025, - 157~170 - 共14页

摘要:). In this paper, we explore the influence of CNN |  neural speaker embedding framework consisting of CNN |  network in a cascade arrangement. The CNN-based feature |  extraction modules considered are: (i) 1-D CNN, (ii | ) vanilla 2D-CNN, and (iii) Selective Kernel Attention
关键词: Speaker verification |  CNN |  Hybrid network |  TDNN-LSTM |  Cosine scoring |  VoxCeleb
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