Titlebook: Advanced Intelligent Computing Technology and Applications; 20th International C De-Shuang Huang,Zhanjun Si,Yijie Pan Conference proceeding

Ischemia

Optometrist

词汇记忆方法

https://doi.org/10.1007/978-3-642-91992-3tion of key information within the graph. Additionally, our Graph Contrastive Learning (GCL) method uniquely eliminates the need for pre-graph augmentation and avoids the requirement of two additional forward trainings in each mini-batch. We name our proposed method MDGCL. Experiments on three publi

Hangar

https://doi.org/10.1007/978-3-322-82433-2opological fusion structures. The paper conducts analyses and summarizes the mechanisms of these two types of structures, introduced the evaluation dataset and evaluation indicators, accompanied by a collation of open-source codes for mainstream feature fusion architectures. Ultimately, through syst

流逝

Variabilität – Ohne Vielfalt keine Evolutionntroduced into the model. Meanwhile, to overcome the issue of excessively long training time for lightweight models, a novel iterative training strategy is proposed to fully unleash the potential of EfficientPose. To validate the effectiveness of EfficientPose model, extensive comparative experiment

Albinism

Variabilität – Ohne Vielfalt keine Evolutionenrich semantic representation. Experimental results demonstrate that compared to the baseline model, DFL-NER achieves F1-score improvements of 7.14%, 0.37%, and 1.81% on the Weibo, Resume, and MSRA datasets, respectively, showcasing outstanding performance.