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Titlebook: Neural Information Processing; 30th International C Biao Luo,Long Cheng,Chaojie Li Conference proceedings 2024 The Editor(s) (if applicable

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發(fā)表于 2025-3-23 09:51:45 | 只看該作者
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發(fā)表于 2025-3-23 16:10:20 | 只看該作者
Knowledge Prompting with?Contrastive Learning for?Unsupervised CommonsenseQAs on stacking large-scale models or extracting knowledge from external sources. However, these methods suffer from either the unstable quality of knowledge or the deficiency in the model’s flexibility. In this paper, we propose a .nowledge .rompting with .ontrastive .earning (KPCL) model to address
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發(fā)表于 2025-3-23 20:17:48 | 只看該作者
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發(fā)表于 2025-3-24 07:14:13 | 只看該作者
Efficient Prompt Tuning for Vision and Language Models method for different downstream tasks is prompt tuning, which fixes the parameters of the visual language model and adjusts only prompt parameters in the process of adapting the downstream tasks, using the knowledge learned by the visual language model during pre-training to solve the problems in t
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發(fā)表于 2025-3-24 11:33:23 | 只看該作者
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發(fā)表于 2025-3-24 20:00:24 | 只看該作者
PLKA-MVSNet: Parallel Multi-view Stereo with?Large Kernel Convolution Attention, particularly the inaccurate depth estimation in challenging areas such as low-texture regions, weak lighting conditions, and non-Lambertian surfaces. We ascribe this problem to the insufficient performance of the feature extractor and the information loss caused by the MVS pipeline transmission, a
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發(fā)表于 2025-3-25 01:05:54 | 只看該作者
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