Transformer-based detectors have shown success in computer vision tasks with
natural images. These models, exemplified by the Deformable DETR, are optimized
through complex engineering strategies tailored to the typical characteristics
of natural scenes. However, medical imaging data presents unique challenges
such as extremely large image sizes, fewer and smaller regions of interest, and
object classes which can be differentiated only through subtle differences.
This study evaluates the applicability of these transformer-based design
choices when applied to a screening mammography dataset that represents these
distinct medical imaging data characteristics. Our analysis reveals that common
design choices from the natural image domain, such as complex encoder
architectures, multi-scale feature fusion, query initialization, and iterative
bounding box refinement, do not improve and sometimes even impair object
detection performance in medical imaging. In contrast, simpler and shallower
architectures often achieve equal or superior results. This finding suggests
that the adaptation of transformer models for medical imaging data requires a
reevaluation of standard practices, potentially leading to more efficient and
specialized frameworks for medical diagnosis.

医学影像数据中的对象检测性能通常不会因为常见的自然图像领域的设计选择而改善，甚至有时会降低，因此适应医学影像数据的变压器模型需要重新评估标准做法，从而可能导致更有效和专门用于医学诊断的框架。

理解应用 DETR 于自然图像与医学图像的差异

Understanding differences in applying DETR to natural and medical images

In recent times, there has been a notable surge in multimodal approaches that
decorates raw LiDAR point clouds with camera-derived features to improve object
detection performance. However, we found that these methods still grapple with
the inherent sparsity of LiDAR point cloud data, primarily because fewer points
are enriched with camera-derived features for sparsely distributed objects. We
present an innovative approach that involves the generation of virtual LiDAR
points using camera images and enhancing these virtual points with semantic
labels obtained from image-based segmentation networks to tackle this issue and
facilitate the detection of sparsely distributed objects, particularly those
that are occluded or distant. Furthermore, we integrate a distance aware data
augmentation (DADA) technique to enhance the models capability to recognize
these sparsely distributed objects by generating specialized training samples.
Our approach offers a versatile solution that can be seamlessly integrated into
various 3D frameworks and 2D semantic segmentation methods, resulting in
significantly improved overall detection accuracy. Evaluation on the KITTI and
nuScenes datasets demonstrates substantial enhancements in both 3D and birds
eye view (BEV) detection benchmarks

通过使用基于相机图像生成虚拟 LiDAR 点，并利用图像分割网络获得的语义标签增强这些虚拟点，以解决稀疏分布的、被遮挡或远离的对象检测问题，并通过生成专门的训练样本的距离感知数据增强技术，将这种方法无缝地整合到各种三维框架和二维语义分割方法中，从而显著提高整体检测准确性，并在 KITTI 和 nuScenes 数据集上的评估中展示了在三维和鸟瞰视角检测基准上的重大改进。

VirtualPainting: 利用虚拟点和距离感知数据增强解决稀疏性的三维物体检测

VirtualPainting: Addressing Sparsity with Virtual Points and  Distance-Aware Data Augmentation for 3D Object Detection

Combining complementary sensor modalities is crucial to providing robust
perception for safety-critical robotics applications such as autonomous driving
(AD). Recent state-of-the-art camera-lidar fusion methods for AD rely on
monocular depth estimation which is a notoriously difficult task compared to
using depth information from the lidar directly. Here, we find that this
approach does not leverage depth as expected and show that naively improving
depth estimation does not lead to improvements in object detection performance
and that, strikingly, removing depth estimation altogether does not degrade
object detection performance. This suggests that relying on monocular depth
could be an unnecessary architectural bottleneck during camera-lidar fusion. In
this work, we introduce a novel fusion method that bypasses monocular depth
estimation altogether and instead selects and fuses camera and lidar features
in a bird's-eye-view grid using a simple attention mechanism. We show that our
model can modulate its use of camera features based on the availability of
lidar features and that it yields better 3D object detection on the nuScenes
dataset than baselines relying on monocular depth estimation.

通过在鸟瞰图格栅中使用简单的注意力机制选择和融合相机和激光雷达特征，我们引入了一种绕过单眼深度估计的新型融合方法，展示了在 nuScenes 数据集上我们的模型可以根据激光雷达特征的可用性调节其使用相机特征，从而在 3D 目标检测方面优于依赖单眼深度估计的基线方法。