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Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2023; 32nd International C Lazaros Iliadis,Antonios Papaleonidas,Chrisina Jay Confe

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樓主: chondrocyte
51#
發(fā)表于 2025-3-30 11:12:19 | 只看該作者
Artificial Neural Networks and Machine Learning – ICANN 2023978-3-031-44210-0Series ISSN 0302-9743 Series E-ISSN 1611-3349
52#
發(fā)表于 2025-3-30 14:45:40 | 只看該作者
53#
發(fā)表于 2025-3-30 17:35:21 | 只看該作者
54#
發(fā)表于 2025-3-30 22:31:15 | 只看該作者
,A Lightweight Multi-Scale Large Kernel Attention Hierarchical Network for?Single Image Deraining,. To address this issue, we propose a lightweight multi-scale large kernel attention hierarchical network(LMANet). Our approach combines multi-scale and Large Kernel Attention(LKA) to create Multi-Scale Large Kernel Attention (MSLKA), where large kernel decomposition can effectively decouple large k
55#
發(fā)表于 2025-3-31 02:37:13 | 只看該作者
A Multi-scale Method for Cell Segmentation in Fluorescence Microscopy Images, expression and the study of cell function. Existing cell segmentation methods have drawbacks in terms of inaccurate location of segmentation boundary, misidentification, and inaccurate segmentation of overlapping cells. To address these issues, a novel . (MMCS) is proposed in this paper. Our motiva
56#
發(fā)表于 2025-3-31 08:51:45 | 只看該作者
57#
發(fā)表于 2025-3-31 11:07:52 | 只看該作者
58#
發(fā)表于 2025-3-31 16:57:27 | 只看該作者
,An Improved Lightweight YOLOv5 for?Remote Sensing Images,s, remains challenging due to the substantial computational demands of existing object detection models. In this paper, we propose an improved remote sensing image small object detection method based on YOLOv5. In order to preserve high-resolution features, we remove the Focus module from the YOLOv5
59#
發(fā)表于 2025-3-31 18:42:24 | 只看該作者
An Improved YOLOv5 with Structural Reparameterization for Surface Defect Detection,g methods for detecting surface defects cannot meet the requirements in terms of speed and accuracy. Based on structural re-parameterization, coordinate attention (CA) mechanism, and an additional detection head, we propose an improved YOLOv5 model for detecting surface defects of steel plates. Firs
60#
發(fā)表于 2025-4-1 00:46:11 | 只看該作者
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