In this paper we address the limitations of the DETR-based semi-supervised object detection
(SSOD) framework particularly focusing on the challenges posed by the quality of object …

Snow is one of the toughest adverse weather conditions for object detection (OD). Currently,
not only there is a lack of snowy OD datasets to train cutting-edge detectors, but also these …

Despite significant progress in semi-supervised learning for image object detection, several
key issues are yet to be addressed for video object detection:(1) Achieving good …

In practice, object detection models used for construction site monitoring exhibit
performance degradation owing to different monitoring settings and dynamic construction …

In the realm of medical imaging, particularly for COVID-19 detection, deep learning models
face substantial challenges such as the necessity for extensive computational resources, the …

As growing usage of social media websites in the recent decades, the amount of news
articles spreading online rapidly, resulting in an unprecedented scale of potentially …

Pseudo-labels are popular in semi-supervised facial expression recognition. Recent
methods usually exploit the confidence as the criterion for pseudo-label generation, and …

The data-driven research paradigm brings a strong demand for training data sharing in
geo⁃ spatial artificial intelligence (GeoAI). The training data content and organization from …

In this paper, we propose a new few-shot object detection (FSOD) framework that introduces
a new contrastive branch to extract the class representation of images, which improves the …

With the ease of accessing large unlabeled datasets, studies on semi-supervised learning
for object detection (SSOD) have become increasingly popular. Among these SSOD studies …