Search Results for author:
Found 7 papers, 3 papers with code
Predicting Adverse Neonatal Outcomes for Preterm Neonates with Multi-Task Learning
In this work, we first analyze the correlations between three adverse neonatal outcomes and then formulate the diagnosis of multiple neonatal outcomes as a multi-task learning (MTL) problem.
VideoXum: Cross-modal Visual and Textural Summarization of Videos
We propose a new joint video and text summarization task.
Out-of-Distribution Detection with Hilbert-Schmidt Independence Optimization
Existing works attempt to solve the problem by explicitly imposing uncertainty on classifiers when OOD inputs are exposed to the classifier during training.
Improving Self-supervised Learning with Automated Unsupervised Outlier Arbitration
Our work reveals a structured shortcoming of the existing mainstream self-supervised learning methods.
CORE-Text: Improving Scene Text Detection with Contrastive Relational Reasoning
In this work, we quantitatively analyze the sub-text problem and present a simple yet effective design, COntrastive RElation (CORE) module, to mitigate that issue.
A Low Rank Promoting Prior for Unsupervised Contrastive Learning
In this paper, we construct a novel probabilistic graphical model that effectively incorporates the low rank promoting prior into the framework of contrastive learning, referred to as LORAC.
Improving Fast Segmentation With Teacher-student Learning
Recently, segmentation neural networks have been significantly improved by demonstrating very promising accuracies on public benchmarks.
