Fully supervised models are predominant in Bayesian active learning. We argue
that their neglect of the information present in unlabelled data harms not just
predictive performance but also decisions about what data to acquire. Our
proposed solution is a simple framework for semi-supervised Bayesian active
learning. We find it produces better-performing models than either conventional
Bayesian active learning or semi-supervised learning with randomly acquired
data. It is also easier to scale up than the conventional approach. As well as
supporting a shift towards semi-supervised models, our findings highlight the
importance of studying models and acquisition methods in conjunction.

全监督模型在贝叶斯主动学习中占主导地位，我们认为它们对未标记数据中的信息的忽视不仅损害了预测性能，也影响了关于获取哪些数据的决策。我们提出了一个简单的半监督贝叶斯主动学习框架，发现它比传统的贝叶斯主动学习或随机获取数据的半监督学习能够生成更好的模型。该框架也更易于扩展。除了支持向半监督模型的转变外，我们的发现还强调了研究模型和获取方法的重要性。

在贝叶斯主动学习中更好地利用无标签数据

Making Better Use of Unlabelled Data in Bayesian Active Learning

Video instance segmentation (VIS) is a challenging vision task that aims to
detect, segment, and track objects in videos. Conventional VIS methods rely on
densely-annotated object masks which are expensive. We reduce the human
annotations to only one point for each object in a video frame during training,
and obtain high-quality mask predictions close to fully supervised models. Our
proposed training method consists of a class-agnostic proposal generation
module to provide rich negative samples and a spatio-temporal point-based
matcher to match the object queries with the provided point annotations.
Comprehensive experiments on three VIS benchmarks demonstrate competitive
performance of the proposed framework, nearly matching fully supervised
methods.

通过减少视频帧中的人工注释点为每个对象，我们提出了一种训练方法来获得接近完全监督模型的高质量掩码预测，在三个视频示例分割基准上展示了该框架的竞争性性能。