Tiny object detection is one of the challenges in the field of object detection, which can be
applied in a variety of fields. Thanks to the advances in deep learning, significant …

While deep learning (DL) is data-hungry and usually relies on extensive labeled data to
deliver good performance, Active Learning (AL) reduces labeling costs by selecting a small …

Active learning is a promising alternative to alleviate the issue of high annotation cost in the
computer vision tasks by consciously selecting more informative samples to label. Active …

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 …

This paper proposes a methodology for building a robust instance segmentation model that
can effectively detect objects on construction sites under various weather conditions. We …

The task of dynamic scene graph generation (SGG) from videos is complicated and
challenging due to the inherent dynamics of a scene, temporal fluctuation of model …

Continued improvements in deep learning architectures have steadily advanced the overall
performance of 3D object detectors to levels on par with humans for certain tasks and …

A desirable data selection algorithm can efficiently choose the most informative samples to
maximize the utility of limited annotation budgets. However, current approaches …

Deep learning has achieved widespread success in medical image analysis, leading to an
increasing demand for large-scale expert-annotated medical image datasets. Yet, the high …

Object detectors trained with weak annotations are affordable alternatives to fully-supervised
counterparts. However, there is still a significant performance gap between them. We …