This paper proposes a scalable technique for develo** lightweight yet powerful models for
object detection in videos using self-training with knowledge distillation. This approach …

3D object detection has recently received much attention due to its great potential in
autonomous vehicle (AV). The success of deep learning based object detectors relies on the …

As the amount of labeled data increases, the performance of deep neural networks tends to
improve. However, annotating a large volume of data can be expensive. Active learning …

The application of deep learning (DL) for high-precision inspection to identify and locate the
positions of each type of steel surface defect has demonstrated considerable potential for …

In practical object detection computer vision applications, training commonly incorporates
multiple data sources. Domain adaptation enhances the models' capacity to generalize …

In mobile robotics, perception in uncontrolled environments like autonomous driving is a
central hurdle. Existing active learning frameworks can help enhance perception by …

Dense prediction tasks such as object detection and segmentation require high-quality
labels at pixel level, which are costly to obtain. Recent advances in foundation models have …

To address the annotation burden in LiDAR-based 3D object detection, active learning (AL)
methods offer a promising solution. However, traditional active learning approaches solely …

The imbalance between positive and negative samples and the loss of small targets in
complex backgrounds are catastrophic for infrared small target detection. To address these …

LiDAR-based 3D object detection is a critical technology for the development of autonomous
driving and robotics. However, the high cost of data annotation limits its advancement. We …