Abstract Multiple Instance Learning (MIL) is a learning paradigm, where training instances
are arranged in sets, called bags, and only bag-level labels are available during training …

Reinforcement learning (RL) has made significant strides in various complex domains.
However, identifying an effective policy via RL often necessitates extensive exploration …

In the last decade, the supply of online video content exploded. Automatic video
summarization has become necessary to allow content consumers to briefly glance at the …

Interpretation of chest radiographs (CXR) is a difficult but essential task for detecting thoracic
abnormalities. Recent artificial intelligence (AI) algorithms have achieved radiologist-level …

As a commonly used model for anomaly detection, the autoencoder model for anomaly
detection does not train the objective for extracted features, which is a downside of …

Deep learning, in recent times, has made remarkable strides when it comes to impressive
performance for many tasks, including medical image processing. One of the contributing …

Acquiring fine-grained object detection annotations in unconstrained images is time-
consuming, expensive, and prone to noise, especially in crowdsourcing scenarios. Most …

Unsupervised anomaly detection (AD) aims to train robust detection models using only
normal samples, while can generalize well to unseen anomalies. Recent research focuses …

Background Thanks to the proliferation of open-source tools, we are seeing an exponential
growth of machine-learning applications, and its integration has become more accessible …

Annotation errors are a challenge not only during training of machine learning models, but
also during their evaluation. Label variations and inaccuracies in datasets often manifest as …