Active learning (AL) is an effective sample selection approach that annotates only a subset
of the training data to address the challenge of data annotation, and deep learning (DL) is …

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 …

Given the large-scale data and the high annotation cost, pretraining-finetuning becomes a
popular paradigm in multiple computer vision tasks. Previous research has covered both the …

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 …

Federated learning enables multiple decentralized clients to learn collaboratively without
sharing local data. However, the expensive annotation cost on local clients remains an …

The performance of learning-based algorithms improves with the amount of labelled data
used for training. Yet, manually annotating data is particularly difficult for medical image …

Active learning (AL) is a promising ML paradigm that has the potential to parse through large
unlabeled data and help reduce annotation cost in domains where labeling entire data can …

We propose a novel semi-supervised active learning framework for monocular 3D object
detection with LiDAR guidance (MonoLiG), which leverages all modalities of collected data …