Supervised 3D Object Detection models have been displaying increasingly
better performance in single-domain cases where the training data comes from
the same environment and sensor as the testing data. However, in real-world
scenarios data from the target domain may not be available for finetuning or
for domain adaptation methods. Indeed, 3D object detection models trained on a
source dataset with a specific point distribution have shown difficulties in
generalizing to unseen datasets. Therefore, we decided to leverage the
information available from several annotated source datasets with our
Multi-Dataset Training for 3D Object Detection (MDT3D) method to increase the
robustness of 3D object detection models when tested in a new environment with
a different sensor configuration. To tackle the labelling gap between datasets,
we used a new label mapping based on coarse labels. Furthermore, we show how we
managed the mix of datasets during training and finally introduce a new
cross-dataset augmentation method: cross-dataset object injection. We
demonstrate that this training paradigm shows improvements for different types
of 3D object detection models. The source code and additional results for this
research project will be publicly available on GitHub for interested parties to
access and utilize: this https URL

通过使用多个标注源数据集，并结合基于粗标签的标签映射以及跨数据集对象注入的数据增强方法，提高了在不同环境中、由不同传感器配置采集的数据上测试时三维物体检测模型的鲁棒性。

MDT3D: 多数据集训练用于 LiDAR 3D 物体检测泛化

MDT3D: Multi-Dataset Training for LiDAR 3D Object Detection  Generalization

We revisit and advance visual prompting (VP), an input prompting technique
for vision tasks. VP can reprogram a fixed, pre-trained source model to
accomplish downstream tasks in the target domain by simply incorporating
universal prompts (in terms of input perturbation patterns) into downstream
data points. Yet, it remains elusive why VP stays effective even given a
ruleless label mapping (LM) between the source classes and the target classes.
Inspired by the above, we ask: How is LM interrelated with VP? And how to
exploit such a relationship to improve its accuracy on target tasks? We peer
into the influence of LM on VP and provide an affirmative answer that a better
'quality' of LM (assessed by mapping precision and explanation) can
consistently improve the effectiveness of VP. This is in contrast to the prior
art where the factor of LM was missing. To optimize LM, we propose a new VP
framework, termed ILM-VP (iterative label mapping-based visual prompting),
which automatically re-maps the source labels to the target labels and
progressively improves the target task accuracy of VP. Further, when using a
contrastive language-image pretrained (CLIP) model, we propose to integrate an
LM process to assist the text prompt selection of CLIP and to improve the
target task accuracy. Extensive experiments demonstrate that our proposal
significantly outperforms state-of-the-art VP methods. As highlighted below, we
show that when reprogramming an ImageNet-pretrained ResNet-18 to 13 target
tasks, our method outperforms baselines by a substantial margin, e.g., 7.9% and
6.7% accuracy improvements in transfer learning to the target Flowers102 and
CIFAR100 datasets. Besides, our proposal on CLIP-based VP provides 13.7% and
7.1% accuracy improvements on Flowers102 and DTD respectively. Our code is
available at this https URL

本论文讨论了思考视觉提示、标签映射和迁移学习等一系列问题，提出了 ILM-VP 和 CLIP-VP 等新的提示方法，并在多个数据集上实现了进一步提升精度的效果。