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Found 7 papers, 0 papers with code
基于框架语义映射和类型感知的篇章事件抽取(Document-Level Event Extraction Based on Frame Semantic Mapping and Type Awareness)
“篇章事件抽取是从给定的文本中识别其事件类型和事件论元。目前篇章事件普遍存在数据稀疏和多值论元耦合的问题。基于此, 本文将汉语框架网(CFN)与中文篇章事件建立映射, 同时引入滑窗机制和触发词释义改善了事件检测的数据稀疏问题;使用基于类型感知标签的多事件分离策略缓解了论元耦合问题。为了提升模型的鲁棒性, 进一步引入对抗训练。本文提出的方法在DuEE-Fin和CCKS2021数据集上实验结果显著优于现有方法。”
Pubic Symphysis-Fetal Head Segmentation Using Pure Transformer with Bi-level Routing Attention
In this paper, we propose a method, named BRAU-Net, to solve the pubic symphysis-fetal head segmentation task.
A Theory of Self-Supervised Framework for Few-Shot Learning
Recently, self-supervised learning (SSL) algorithms have been applied to Few-shot learning(FSL).
A Survey on Machine Learning from Few Samples
The capability of learning and generalizing from very few samples successfully is a noticeable demarcation separating artificial intelligence and human intelligence since humans can readily establish their cognition to novelty from just a single or a handful of examples whereas machine learning algorithms typically entail hundreds or thousands of supervised samples to guarantee generalization ability.
Self-reinforcing Unsupervised Matching
Remarkable gains in deep learning usually rely on tremendous supervised data.
An In-field Automatic Wheat Disease Diagnosis System
Crop diseases are responsible for the major production reduction and economic losses in agricultural industry world- wide.
Zero-Shot Learning by Generating Pseudo Feature Representations
Given the dataset of seen classes and side information of unseen classes (e. g. attributes), we synthesize feature-level pseudo representations for novel concepts, which allows us access to the formulation of unseen class predictor.
