Search Results for author:
Found 8 papers, 2 papers with code
Multi Self-supervised Pre-fine-tuned Transformer Fusion for Better Intelligent Transportation Detection
To address these problems, we propose a Multi Self-supervised Pre-fine-tuned Transformer Fusion (MSPTF) network, consisting of two steps: unsupervised pre-fine-tune domain knowledge learning and multi-model fusion target task learning.
Road Disease Detection based on Latent Domain Background Feature Separation and Suppression
Road disease detection is challenging due to the the small proportion of road damage in target region and the diverse background, which introduce lots of domain information. Besides, disease categories have high similarity, makes the detection more difficult.
MFL-YOLO: An Object Detection Model for Damaged Traffic Signs
The Grad-CAM heat map visualization shows that our model can better focus on the local details of the damaged traffic signs.
Integrating GAN and Texture Synthesis for Enhanced Road Damage Detection
In addition to using GAN to generate damage with various shapes, we further employ texture synthesis techniques to extract road textures.
Label Correction Model for Aspect-based Sentiment Analysis
Specifically, given an input sentence, our model first predicts the aspect boundary label sequence and sentiment label sequence, then re-predicts the aspect boundary (sentiment) label sequence using the embeddings of the previously predicted sentiment (aspect boundary) label.
Fine-tuning ERNIE for chest abnormal imaging signs extraction
In this paper, we formulate chest abnormal imaging sign extraction as a sequence tagging and matching problem.
Causality Extraction based on Self-Attentive BiLSTM-CRF with Transferred Embeddings
Causality extraction from natural language texts is a challenging open problem in artificial intelligence.
A Self-Attentive Hierarchical Model for Jointly Improving Text Summarization and Sentiment Classification
This model jointly performs abstractive text summarization and sentiment classification within a hierarchical end-to-end neural framework, in which the sentiment classification layer on top of the summarization layer predicts the sentiment label in the light of the text and the generated summary.
