In this paper, we present a new data-efficient voxel-based self-supervised
learning method for event cameras. Our pre-training overcomes the limitations
of previous methods, which either sacrifice temporal information by converting
event sequences into 2D images for utilizing pre-trained image models or
directly employ paired image data for knowledge distillation to enhance the
learning of event streams. In order to make our pre-training data-efficient, we
first design a semantic-uniform masking method to address the learning
imbalance caused by the varying reconstruction difficulties of different
regions in non-uniform data when using random masking. In addition, we ease the
traditional hybrid masked modeling process by explicitly decomposing it into
two branches, namely local spatio-temporal reconstruction and global semantic
reconstruction to encourage the encoder to capture local correlations and
global semantics, respectively. This decomposition allows our selfsupervised
learning method to converge faster with minimal pre-training data. Compared to
previous approaches, our self-supervised learning method does not rely on
paired RGB images, yet enables simultaneous exploration of spatial and temporal
cues in multiple scales. It exhibits excellent generalization performance and
demonstrates significant improvements across various tasks with fewer
parameters and lower computational costs.

我们提出了一种新的数据高效的基于体素的自监督学习方法，克服了以往方法的局限性，该方法通过预训练克服了通过将事件序列转换为 2D 图像以利用预训练图像模型，或直接使用成对图像数据进行知识蒸馏以增强事件流学习的方法所带来的对时间信息的牺牲。我们的自监督学习方法不依赖于成对的 RGB 图像，且能够在多个尺度上同时探索空间和时间线索，展现出卓越的泛化性能，在各种任务中显著改善参数更少、计算成本更低。

数据高效的事件相机预训练通过解耦蒙版建模

Data-efficient Event Camera Pre-training via Disentangled Masked  Modeling

Mechanical metamaterial is a synthetic material that can possess
extraordinary physical characteristics, such as abnormal elasticity, stiffness,
and stability, by carefully designing its internal structure. To make
metamaterials contain delicate local structures with unique mechanical
properties, it is a potential method to represent them through high-resolution
voxels. However, it brings a substantial computational burden. To this end,
this paper proposes a fast inverse design method, whose core is an advanced
deep generative AI algorithm, to generate voxel-based mechanical metamaterials.
Specifically, we use the self-conditioned diffusion model, capable of
generating a microstructure with a resolution of $128^3$ to approach the
specified homogenized tensor matrix in just 3 seconds. Accordingly, this rapid
reverse design tool facilitates the exploration of extreme metamaterials, the
sequence interpolation in metamaterials, and the generation of diverse
microstructures for multi-scale design. This flexible and adaptive generative
tool is of great value in structural engineering or other mechanical systems
and can stimulate more subsequent research.

此论文介绍了一种快速反向设计方法，利用先进的深度生成人工智能算法生成基于体素的机械超材料，该方法能够在仅 3 秒内逼近所指定的均质张量矩阵的分辨率为 $128^3$ 的微结构。该工具对于结构工程或其他机械系统具有灵活和自适应的生成能力，可以激发更多的后续研究。

密度驱动机械超材料快速逆向设计的引导扩散

Guided Diffusion for Fast Inverse Design of Density-based Mechanical  Metamaterials

Most of the existing single-stage and two-stage 3D object detectors are
anchor-based methods, while the efficient but challenging anchor-free
single-stage 3D object detection is not well investigated. Recent studies on 2D
object detection show that the anchor-free methods also are of great potential.
However, the unordered and sparse properties of point clouds prevent us from
directly leveraging the advanced 2D methods on 3D point clouds. We overcome
this by converting the voxel-based sparse 3D feature volumes into the sparse 2D
feature maps. We propose an attentive module to fit the sparse feature maps to
dense mostly on the object regions through the deformable convolution tower and
the supervised mask-guided attention. By directly regressing the 3D bounding
box from the enhanced and dense feature maps, we construct a novel single-stage
3D detector for point clouds in an anchor-free manner. We propose an IoU-based
detection confidence re-calibration scheme to improve the correlation between
the detection confidence score and the accuracy of the bounding box regression.
Our code is publicly available at https://github.com/jialeli1/MGAF-3DSSD.

提出了一种基于稀疏特征图的关注模块的单级无 Anchor 3D 对象检测方法，通过将基于体素的稀疏 3D 特征数据转换为稀疏 2D 特征图，直接从增强和稠密的 2D 特征映射中回归出 3D 边界框，并进一步提出了一种基于 IoU 的检测置信度重新校准方案，最终可用于点云数据的目标检测。