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Titlebook: Advanced Intelligent Computing Technology and Applications; 20th International C De-Shuang Huang,Chuanlei Zhang,Wei Chen Conference proceed

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樓主: 皺紋
51#
發(fā)表于 2025-3-30 11:23:03 | 只看該作者
Prompt-Based Event Temporal Relation Extraction with Contrastive Learningirs of events within a sentence. Existing models for extracting temporal relations treat it as a supervised classification task with Generative Pre-trained Transformer (GPT). However, due to the complexity of annotation, most models face the challenge of insufficient labeled data. Furthermore, the c
52#
發(fā)表于 2025-3-30 14:14:27 | 只看該作者
53#
發(fā)表于 2025-3-30 17:05:58 | 只看該作者
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發(fā)表于 2025-3-30 22:09:04 | 只看該作者
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發(fā)表于 2025-3-31 01:26:44 | 只看該作者
56#
發(fā)表于 2025-3-31 07:54:07 | 只看該作者
57#
發(fā)表于 2025-3-31 09:47:42 | 只看該作者
Multi-label Classification for Concrete Defects Based on EfficientNetV2Spatial Pyramid Pooling Fast (SPPF), to extract defect-representative features from concrete defect images based on EfficientNetV2. Among them, SCARM contributes to assisting the network focus on crucial features and suppressing unnecessary ones and SPPF used to aggregate multi-scale features to add
58#
發(fā)表于 2025-3-31 14:24:10 | 只看該作者
59#
發(fā)表于 2025-3-31 19:23:07 | 只看該作者
Designing Real-Time Neural Networks by Efficient Neural Architecture Searchch efficiency and achieves a success rate of approximately 99.2% in meeting time constraints, outperforming other methods. Our experiments also show that CNNs designed by RetNAS surpass the accuracy of manually designed CNNs and visual transformers by 0.4% to 5%.
60#
發(fā)表于 2025-4-1 00:53:29 | 只看該作者
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