Brain networks characterize complex connectivities among brain regions as
graph structures, which provide a powerful means to study brain connectomes. In
recent years, graph neural networks have emerged as a prevalent paradigm of
learning with structured data. However, most brain network datasets are limited
in sample sizes due to the relatively high cost of data acquisition, which
hinders the deep learning models from sufficient training. Inspired by
meta-learning that learns new concepts fast with limited training examples,
this paper studies data-efficient training strategies for analyzing brain
connectomes in a cross-dataset setting. Specifically, we propose to meta-train
the model on datasets of large sample sizes and transfer the knowledge to small
datasets. In addition, we also explore two brain-network-oriented designs,
including atlas transformation and adaptive task reweighing. Compared to other
pre-training strategies, our meta-learning-based approach achieves higher and
stabler performance, which demonstrates the effectiveness of our proposed
solutions. The framework is also able to derive new insights regarding the
similarities among datasets and diseases in a data-driven fashion.

通过元学习技术，在跨数据集的情况下，训练大规模的大脑连通性图谱数据集，提出了基于元学习的数据高效训练策略，并通过图神经网络框架，包括变换式的跨数据集任务权重自适应等，得到了更高效和更稳定的性能，为了更好地研究疾病间和数据集间的相似性，提出了新的见解。

基于多任务元学习的数据高效脑连接组分析

Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learning

Motivated by the need for estimating the 3D pose of arbitrary objects, we
consider the challenging problem of class-agnostic object viewpoint estimation
from images only, without CAD model knowledge. The idea is to leverage features
learned on seen classes to estimate the pose for classes that are unseen, yet
that share similar geometries and canonical frames with seen classes. We train
a direct pose estimator in a class-agnostic way by sharing weights across all
object classes, and we introduce a contrastive learning method that has three
main ingredients: (i) the use of pre-trained, self-supervised, contrast-based
features; (ii) pose-aware data augmentations; (iii) a pose-aware contrastive
loss. We experimented on Pascal3D+, ObjectNet3D and Pix3D in a cross-dataset
fashion, with both seen and unseen classes. We report state-of-the-art results,
including against methods that additionally use CAD models as input.

研究利用对已知物体类别学习到的特征来估算未知类别物体的三维姿态，提出无需 CAD 模型知识的类不可知对象视角估计的对比学习方法，实验结果表明该方法在多个数据集上具有最佳表现，包括使用 CAD 模型作为输入的方法。