Video scene graph generation (VidSGG) aims to parse the video content into scene graphs,
which involves modeling the spatio-temporal contextual information in the video. However …

The distributional shift issue between training and test sets is a critical challenge in machine
learning, and is aggravated when models capture unintended decision rules with spurious …

The pervasive integration of machine learning (ML) across various sectors has underscored
the critical challenge of addressing inherent biases in ML models. These biases not only …

The problem of bias in facial affect recognition tools can lead to severe consequences and
issues. It has been posited that causality is able to address the gaps induced by the …

Recently, molecular relational learning, whose goal is to predict the interaction behavior
between molecular pairs, got a surge of interest in molecular sciences due to its wide range …

Deep learning models can be vulnerable to recovery attacks, raising privacy concerns to
users, and widespread algorithms such as empirical risk minimization (ERM) often do not …

Supervised training of existing deep learning models for sequence labeling relies on large
scale labeled datasets. Such datasets are generally created with crowd-source labeling …

Recently, generalization on out-of-distribution (OOD) data with correlation shift has attracted
great attentions. The correlation shift is caused by the spurious attributes that correlate to the …

Applying neural models injected with in-domain user and product information to learn review
representations of unseen or anonymous users incurs an obvious obstacle in content-based …

While most continual learning algorithms have focused on tackling the stability-plasticity
dilemma, they have overlooked the effects of the knowledge transfer when the dataset is …