We propose a method that augments a simulated dataset using diffusion models
to improve the performance of pedestrian detection in real-world data. The high
cost of collecting and annotating data in the real-world has motivated the use
of simulation platforms to create training datasets. While simulated data is
inexpensive to collect and annotate, it unfortunately does not always closely
match the distribution of real-world data, which is known as the sim2real gap.
In this paper we propose a novel method of synthetic data creation meant to
close the sim2real gap for the challenging pedestrian detection task. Our
method uses a diffusion-based architecture to learn a real-world distribution
which, once trained, is used to generate datasets. We mix this generated data
with simulated data as a form of augmentation and show that training on a
combination of generated and simulated data increases average precision by as
much as 27.3% for pedestrian detection models in real-world data, compared
against training on purely simulated data.

该研究提出了一种使用扩散模型增强模拟数据集的方法，以改善行人检测在现实世界数据中的性能，并使用扩散基础架构来生成具有真实分布的数据集，以填补模拟数据和真实数据之间的差距，通过使用生成数据与模拟数据进行的训练可以将行人检测模型在真实数据中的平均精度提高 27.3%。

扩散式数据集生成：缩小行人检测 Sim2Real 差距的步伐

Diffusion Dataset Generation: Towards Closing the Sim2Real Gap for  Pedestrian Detection

Cryo-electron tomography (cryo-ET) is an imaging technique that allows
three-dimensional visualization of macro-molecular assemblies under near-native
conditions. Cryo-ET comes with a number of challenges, mainly low
signal-to-noise and inability to obtain images from all angles. Computational
methods are key to analyze cryo-electron tomograms.
To promote innovation in computational methods, we generate a novel simulated
dataset to benchmark different methods of localization and classification of
biological macromolecules in tomograms. Our publicly available dataset contains
ten tomographic reconstructions of simulated cell-like volumes. Each volume
contains twelve different types of complexes, varying in size, function and
structure.
In this paper, we have evaluated seven different methods of finding and
classifying proteins. Seven research groups present results obtained with
learning-based methods and trained on the simulated dataset, as well as a
baseline template matching (TM), a traditional method widely used in cryo-ET
research. We show that learning-based approaches can achieve notably better
localization and classification performance than TM. We also experimentally
confirm that there is a negative relationship between particle size and
performance for all methods.

本文介绍了为了促进计算方法的创新，生成了一个新的模拟数据集，用于评估在 cryo - 电子断层扫描中蛋白质的定位和分类的不同方法。我们评估了七种不同的方法，包括基于学习的方法和传统方法，结果表明学习方法比传统方法具有更好的定位和分类性能，并且所有方法的性能与粒子大小之间存在负相关关系。

SHREC 2021: 冷冻电子断层扫描的分类

SHREC 2021: Classification in cryo-electron tomograms

Vehicle-to-everything (V2X) communication techniques enable the collaboration
between vehicles and many other entities in the neighboring environment, which
could fundamentally improve the perception system for autonomous driving.
However, the lack of a public dataset significantly restricts the research
progress of collaborative perception. To fill this gap, we present V2X-Sim, a
comprehensive simulated multi-agent perception dataset for V2X-aided autonomous
driving. V2X-Sim provides: (1) \hl{multi-agent} sensor recordings from the
road-side unit (RSU) and multiple vehicles that enable collaborative
perception, (2) multi-modality sensor streams that facilitate multi-modality
perception, and (3) diverse ground truths that support various perception
tasks. Meanwhile, we build an open-source testbed and provide a benchmark for
the state-of-the-art collaborative perception algorithms on three tasks,
including detection, tracking and segmentation. V2X-Sim seeks to stimulate
collaborative perception research for autonomous driving before realistic
datasets become widely available. Our dataset and code are available at
https://ai4ce.github.io/V2X-Sim/.

通过提出 V2X-Sim，一个完整的模拟多智能体感知数据集，本文试图为自动驾驶的协作式感知研究提供一个 benchmark，其中包含了多节点感知、多模态感知以及各种各样的真实解等任务，并为最前沿的协作式感知算法提供一个基准。