Semi-supervised learning has greatly advanced medical image segmentation since it
effectively alleviates the need of acquiring abundant annotations from experts, wherein the …

Spatio-temporal video grounding aims to localize the aligned visual tube corresponding to a
language query. Existing techniques achieve such alignment by exploiting dense boundary …

Temporal grounding in videos aims to localize one target video segment that semantically
corresponds to a given query sentence. Thanks to the semantic diversity of natural language …

Active learning (AL) is an effective sample selection approach that annotates only a subset
of the training data to address the challenge of data annotation, and deep learning (DL) is …

The task of multimodal cancer detection is to determine the locations and categories of
lesions by using different imaging techniques, which is one of the key research methods for …

Abstract Active Domain Adaptation (ADA) aims to maximally boost model adaptation in a
new target domain by actively selecting a limited number of target data to annotate. This …

Automatic polyp segmentation from colonoscopy images is an essential prerequisite for the
development of computer-assisted therapy. However, the complex semantic information and …

Device Model Generalization (DMG) is a practical yet under-investigated research topic for
on-device machine learning applications. It aims to improve the generalization ability of pre …

Semi-supervised learning (SSL) has attracted enormous attention due to its vast potential of
mitigating the dependence on large labeled datasets. The latest methods (eg, FixMatch) use …

Modern online platforms are increasingly employing recommendation systems to address
information overload and improve user engagement. There is an evolving paradigm in this …