Abstract Although Domain Adaptation in Semantic Scene Segmentation has shown
impressive improvement in recent years, the fairness concerns in the domain adaptation …

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 …

Diabetic retinopathy (DR) and diabetic macular edema (DME) are leading causes of
permanent blindness worldwide. Designing an automatic grading system with good …

Continual semantic segmentation aims to learn new classes while maintaining the
information from the previous classes. Although prior studies have shown impressive …

In video person re-identification (Re-ID), the network must consistently extract features of the
target person from successive frames. Existing methods tend to focus only on how to use …

Empirical risk minimization (ERM) is sensitive to spurious correlations present in training
data, which poses a significant risk when deploying systems trained under this paradigm in …

Removing the noise and improving the visual quality of hyperspectral images (HSIs) is
challenging in academia and industry. Great efforts have been made to leverage local …

Learned recommender systems may inadvertently leak information about their training data,
leading to privacy violations. We investigate privacy threats faced by recommender systems …

In the field of music information retrieval (MIR), cover song identification (CSI) is a
challenging task that aims to identify cover versions of a query song from a massive …

We propose an information-theoretic bias measurement technique through a causal
interpretation of spurious correlation, which is effective to identify the feature-level …