Serial femtosecond crystallography at X-ray free electron laser facilities
opens a new era for the determination of crystal structure. However, the data
processing of those experiments is facing unprecedented challenge, because the
total number of diffraction patterns needed to determinate a high-resolution
structure is huge. Machine learning methods are very likely to play important
roles in dealing with such a large volume of data. Convolutional neural
networks have made a great success in the field of pattern classification,
however, training of the networks need very large datasets with labels. Th is
heavy dependence on labeled datasets will seriously restrict the application of
networks, because it is very costly to annotate a large number of diffraction
patterns. In this article we present our job on the classification of
diffraction pattern by weakly supervised algorithms, with the aim of reducing
as much as possible the size of the labeled dataset required for training. Our
result shows that weakly supervised methods can significantly reduce the need
for the number of labeled patterns while achieving comparable accuracy to fully
supervised methods.

通过弱监督算法对衍射图案进行分类，可以显著减少训练所需的标记样本数量，同时获得与全监督方法相当的准确性，为解决大量数据处理的挑战提供了一种可能。

序贯飞秒晶体学中模式分类的弱监督学习

Weakly supervised learning for pattern classification in serial  femtosecond crystallography

With the advent of the big model era, the demand for data has become more
important. Especially in monocular 3D object detection, expensive manual
annotations potentially limit further developments. Existing works have
investigated weakly supervised algorithms with the help of LiDAR modality to
generate 3D pseudo labels, which cannot be applied to ordinary videos. In this
paper, we propose a novel paradigm, termed as BA$^2$-Det, leveraging the idea
of global-to-local 3D reconstruction for 2D supervised monocular 3D object
detection. Specifically, we recover 3D structures from monocular videos by
scene-level global reconstruction with global bundle adjustment (BA) and obtain
object clusters by the DoubleClustering algorithm. Learning from completely
reconstructed objects in global BA, GBA-Learner predicts pseudo labels for
occluded objects. Finally, we train an LBA-Learner with object-centric local BA
to generalize the generated 3D pseudo labels to moving objects. Experiments on
the large-scale Waymo Open Dataset show that the performance of BA$^2$-Det is
on par with the fully-supervised BA-Det trained with 10% videos and even
outperforms some pioneer fully-supervised methods. We also show the great
potential of BA$^2$-Det for detecting open-set 3D objects in complex scenes.
The code will be made available. Project page: this https URL .

本研究提出了一种新的逆向学习范式，称为 BA^2-Det，利用全局到局部 3D 重建的思想，对二维监督单目三维物体检测进行学习，实验结果显示 BA^2-Det 的性能与完全监督方法相当，并且甚至优于某些先驱的完全监督方法。