Object detection (OD), a crucial vision task, remains challenged by the lack of large training
datasets with precise object localization labels. In this work, we propose ALWOD, a new …

Autonomous vehicle (AV) systems rely on robust perception models as a cornerstone of
safety assurance. However objects encountered on the road exhibit a long-tailed distribution …

Oriented object detection has witnessed significant progress in recent years. However the
impressive performance of oriented object detectors is at the huge cost of labor-intensive …

Despite the remarkable progress of general object detection, the lack of labeled aerial
images limits the robustness and generalization of the detector. Teacher–student learning is …

Current sparsely-supervised object detection methods largely depend on high threshold
settings to derive high-quality pseudo labels from detector predictions. However hard …

Semi-supervised object detection (SSOD), leveraging unlabeled data to boost object
detectors, has become a hot topic recently. However, existing SSOD approaches mainly …

The accurate detection of relevant vehicles, pedestrians, and other targets on the road plays
a crucial role in ensuring the safety of autonomous driving. In recent years, object detectors …

Multimodal sentiment analysis holds significant importance within the realm of human-
computer interaction. Due to the ease of collecting unlabeled online resources compared to …

Abstract Semi-Supervised Object Detection (SSOD) has emerged as a potent framework that
leverages unlabeled data to reduce annotation costs while enhancing model performance …

Alleviating noisy pseudo labels remains a key challenge in Semi-Supervised Temporal
Action Localization (SS-TAL). Existing methods often filter pseudo labels based on strict …