Context‐guided ground truth sampling for multi‐modality data augmentation in autonomous driving

TL;DR: A self-supervised method and a corresponding package to classify driving scenes in large-scale point cloud data using a contrastive learning approach which encodes each single frame of the collected raw data into a feature vector that can be effectively classified.

TL;DR: The defect detection of printed circuit board (PCB) images faces challenges such as limited sample number, imbalanced sample types, and varying detection reliability, so an uncertainty-aware unsupervised detection model on PCB images is proposed.

TL;DR: This study introduces a multimodal seed data augmentation model to enhance intelligent recognition and comprehension of Latin Cuengh, a low-resource dialect, by leveraging speech and bilingual textual data, achieving a 3% accuracy increase and improved training efficiency.

TL;DR: Limited annotations in 3D object detection for autonomous factory driving are addressed through ground truth sampling augmentation. The effectiveness of various augmentation techniques is explored, revealing that polygon-based augmentation yields the best results.

TL;DR: A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.

TL;DR: The autonomous driving platform is used to develop novel challenging benchmarks for the tasks of stereo, optical flow, visual odometry/SLAM and 3D object detection, revealing that methods ranking high on established datasets such as Middlebury perform below average when being moved outside the laboratory to the real world.

TL;DR: This work introduces Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling, and exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity.

TL;DR: nuScenes as discussed by the authors is the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view.