Abstract\emph {Classification with rejection}(CwR) refrains from making a prediction to avoid
critical misclassification when encountering test samples that are difficult to classify. Though …

Abstract We propose the Model Quality Network, MQ-Net in short, for predicting the quality,
eg the pose error of essential matrices, of models generated inside RANSAC. It replaces the …

The use of deep neural networks in real-world applications require well-calibrated networks
with confidence scores that accurately reflect the actual probability. However, it has been …

Though significant progress has been achieved on fine-grained visual classification (FGVC),
severe overfitting still hinders model generalization. A recent study shows that hard samples …

The problem of calibrating deep neural networks (DNNs) for multi-label learning is
considered. It is well-known that DNNs trained by cross-entropy for single-label or one-hot …

Reliable confidence estimation is a challenging yet fundamental requirement in many risk-
sensitive applications. However, modern deep neural networks are often overconfident for …

Graphs can model real-world, complex systems by representing entities and their
interactions in terms of nodes and edges. To better exploit the graph structure, graph neural …

Multi-task visual recognition plays a pivotal role in addressing the composite challenges
encountered during the monitoring of crop health, pest infestations, and disease outbreaks …

Importance weighting is a fundamental procedure in statistics and machine learning that
weights the objective function or probability distribution based on the importance of the …

This paper delves into the confidence calibration in prediction when using Graph Neural
Networks (GNNs), which has emerged as a notable challenge in the field. Despite their …