Deep learning models for object detection in autonomous driving have recently achieved impressive performance gains and are already being deployed in vehicles worldwide. However, current models require increasingly large datasets for training. Acquiring and labeling such data is costly, necessitating the development of new strategies to optimize this process. Active learning is a promising approach that has been extensively researched in the image domain. In our work, we extend this concept to the LiDAR domain by developing several inconsistency-based sample selection strategies and evaluate their effectiveness in various settings. Our results show that using a naive inconsistency approach based on the number of detected boxes, we achieve the same mAP as the random sampling strategy with 50% of the labeled data.

Ultrasound imaging holds significant promise for the observation of molecular and cellular phenomena through the utilization of acoustic contrast agents and acoustic reporter genes. Optimizing imaging methodologies for enhanced detection represents an imperative advancement in this field. Most advanced techniques relying on amplitude modulation schemes such as cross amplitude modulation (xAM) and ultrafast amplitude modulation (uAM) combined with Hadamard encoded multiplane wave transmissions have shown efficacy in capturing the acoustic signals of gas vesicles (GVs). Nonetheless, uAM sequence requires odd- or even-element transmissions leading to imprecise amplitude modulation emitting scheme, and the complex multiplane wave transmission scheme inherently yields overlong pulse durations. xAM sequence is limited in terms of field of view and imaging depth. To overcome these limitations, we introduce an innovative ultrafast imaging sequence called amplitude-modulated singular value decomposition (SVD) processing. Our method demonstrates a contrast imaging sensitivity comparable to the current gold-standard xAM and uAM, while requiring 4.8 times fewer pulse transmissions. With a similar number of transmit pulses, amplitude-modulated SVD outperforms xAM and uAM in terms of an improvement in signal-to-background ratio of $+ 4.78~\pm ~0.35$ dB and $+ 8.29~\pm ~3.52$ dB, respectively. Furthermore, the method exhibits superior robustness across a wide range of acoustic pressures and enables high-contrast imaging in ex vivo and in vivo settings. Furthermore, amplitude-modulated SVD is envisioned to be applicable for the detection of slow moving microbubbles in ultrasound localization microscopy (ULM).

Today, deep learning detectors for autonomous driving are delivering impressive results on public datasets and in real-world applications. However, these detectors require large amounts of data, especially labeled data, to achieve the performance needed to ensure safe driving. The process of collecting and tagging data is expensive and cumbersome. Therefore, the recent focus of the industry has been on how to achieve similar performance while limiting the amount of labeled data required to train such models. Within the cross-modal active learning paradigm, we propose and analyze new strategies to exploit the inconsistencies between camera and LiDAR detectors to improve sampling efficiency and label only the samples that promise improvements for model training. For this, we leverage the 2D projection of the bounding boxes to equalize the output quality of camera and LiDAR detections. Finally, we achieve up to 0.6% AP improvement for camera and 2% improvement for LiDAR over random sampling on the KITTI dataset using a sampling strategy based on the number of detected objects.