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Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives
The perception of the surrounding environment is a key requirement for autonomous driving
systems, yet the computation of an accurate semantic representation of the scene starting …
systems, yet the computation of an accurate semantic representation of the scene starting …
Activenerf: Learning where to see with uncertainty estimation
Abstract Recently, Neural Radiance Fields (NeRF) has shown promising performances on
reconstructing 3D scenes and synthesizing novel views from a sparse set of 2D images …
reconstructing 3D scenes and synthesizing novel views from a sparse set of 2D images …
Virtual kitti 2
This paper introduces an updated version of the well-known Virtual KITTI dataset which
consists of 5 sequence clones from the KITTI tracking benchmark. In addition, the dataset …
consists of 5 sequence clones from the KITTI tracking benchmark. In addition, the dataset …
A survey on deep learning based methods and datasets for monocular 3D object detection
Owing to recent advancements in deep learning methods and relevant databases, it is
becoming increasingly easier to recognize 3D objects using only RGB images from single …
becoming increasingly easier to recognize 3D objects using only RGB images from single …
Active learning for deep visual tracking
Convolutional neural networks (CNNs) have been successfully applied to the single target
tracking task in recent years. Generally, training a deep CNN model requires numerous …
tracking task in recent years. Generally, training a deep CNN model requires numerous …
Reducing label effort: Self-supervised meets active learning
Active learning is a paradigm aimed at reducing the annotation effort by training the model
on actively selected informative and/or representative samples. Another paradigm to reduce …
on actively selected informative and/or representative samples. Another paradigm to reduce …
Synthetic datasets for autonomous driving: A survey
Autonomous driving techniques have been flourishing in recent years while thirsting for
huge amounts of high-quality data. However, it is difficult for real-world datasets to keep up …
huge amounts of high-quality data. However, it is difficult for real-world datasets to keep up …
Nothing stands still: A spatiotemporal benchmark on 3d point cloud registration under large geometric and temporal change
Building 3D geometric maps of man-made spaces is a well-established and active field that
is fundamental to numerous computer vision and robotics applications. However …
is fundamental to numerous computer vision and robotics applications. However …
Hybrid active learning via deep clustering for video action detection
In this work, we focus on reducing the annotation cost for video action detection which
requires costly frame-wise dense annotations. We study a novel hybrid active learning (AL) …
requires costly frame-wise dense annotations. We study a novel hybrid active learning (AL) …
Class-balanced active learning for image classification
Active learning aims to reduce the labeling effort that is required to train algorithms by
learning an acquisition function selecting the most relevant data for which a label should be …
learning an acquisition function selecting the most relevant data for which a label should be …