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 …

Weakly supervised object detection (WSOD) has received widespread attention since it
requires only image-category annotations for detector training. Many advanced approaches …

Weakly supervised object localization (WSOL) remains challenging when learning object
localization models from image category labels. Conventional methods that discriminatively …

Weakly supervised object detection (WSOD) in remote sensing images (RSIs) has good
practical value because it only requires the image-level annotations. The existing methods …

Weakly supervised object detection in remote sensing images (RSI) is still a challenge
because of the lack of instance-level labels, and many existing methods have two problems …

Weakly supervised object detection (WSOD) aims to train detectors using only image-
category labels. Current methods typically first generate dense class-agnostic proposals and …

Deep learning (DL) has demonstrated remarkable performance in the classification of
hyperspectral images (HSIs) by leveraging its powerful ability to automatically learn deep …

Abstract Online Social Networks (OSNs) are a popular platform for communication and
collaboration. Spammers are highly active in OSNs. Uncovering spammers has become one …

Artificial intelligence (AI)-empowered defect detection has emerged as a promising solution
for enhancing quality control in manufacturing. While prevalent object detection-based …

In this paper, we tackle the problem called learning from label proportions (LLP), where the
training data is arranged into various bags, with only the proportions of different categories …