Existing semantic segmentation approaches are often limited by costly pixel-wise
annotations and predefined classes. In this work, we present CLIP-S^ 4 that leverages self …

Recent advances in computer vision are primarily driven by the usage of deep learning,
which is known to require large amounts of data, and creating datasets for this purpose is …

Abstract Video Object Segmentation (VOS) is crucial for several applications, from video
editing to video data generation. Training a VOS model requires an abundance of manually …

Although interactive image segmentation techniques have made significant progress,
supervised learning-based methods rely heavily on large-scale labeled data which is …

In the field of image classification, existing methods often struggle with biased or ambiguous
data, a prevalent issue in real-world scenarios. Current strategies, including semi …

High-quality data is crucial for the success of machine learning, but labeling large datasets
is often a time-consuming and costly process. While semi-supervised learning can help …

While numerous methods exist to solve classification problems within curated datasets,
these solutions often fall short in biomedical applications due to the biased or ambiguous …

Multisource Private Label Propagation (MPLP) is designed for different organizations to
collaboratively predict labels of unlabeled nodes through iterative propagation and label …

In DARPA's view of the three waves of AI, the first wave of AI, symbolic AI, focused on explicit
knowledge. The second and current wave of AI is termed statistical AI. Deep learning …

Collecting image annotations remains a significant burden when deploying CNN in a
specific applicative context. This is especially the case when the annotation consists in …