Category-level pose estimation is a challenging task with many potential
applications in computer vision and robotics. Recently, deep-learning-based
approaches have made great progress, but are typically hindered by the need for
large datasets of either pose-labelled real images or carefully tuned
photorealistic simulators. This can be avoided by using only geometry inputs
such as depth images to reduce the domain-gap but these approaches suffer from
a lack of semantic information, which can be vital in the pose estimation
problem. To resolve this conflict, we propose to utilize both geometric and
semantic features obtained from a pre-trained foundation model.Our approach
projects 2D features from this foundation model into 3D for a single object
model per category, and then performs matching against this for new single view
observations of unseen object instances with a trained matching network. This
requires significantly less data to train than prior methods since the semantic
features are robust to object texture and appearance. We demonstrate this with
a rich evaluation, showing improved performance over prior methods with a
fraction of the data required.

类别级别姿态估计是一项具有挑战性的任务，近期深度学习方法取得了很大进展，但常常受到需要大量数据集或精心调整的逼真模拟器的限制。为了解决这个冲突，我们提出利用从预训练基础模型中获取的几何和语义特征，通过将 2D 特征从基础模型投影到三维空间来对单个类别的物体模型进行匹配，并在训练好的匹配网络上处理对未见物体实例的新的单视图观测，这比先前方法需要的数据量显著减少。我们通过丰富的评估结果表明了这一点，并展示了比先前方法更好的性能。

GS-Pose: 基于几何和语义一致的类别级目标姿态估计

GS-Pose: Category-Level Object Pose Estimation via Geometric and  Semantic Correspondence

Semi-supervised learning is a promising way to reduce the annotation cost for
text-classification. Combining with pre-trained language models (PLMs), e.g.,
BERT, recent semi-supervised learning methods achieved impressive performance.
In this work, we further investigate the marriage between semi-supervised
learning and a pre-trained language model. Unlike existing approaches that
utilize PLMs only for model parameter initialization, we explore the inherent
topic matching capability inside PLMs for building a more powerful
semi-supervised learning approach. Specifically, we propose a joint
semi-supervised learning process that can progressively build a standard
$K$-way classifier and a matching network for the input text and the Class
Semantic Representation (CSR). The CSR will be initialized from the given
labeled sentences and progressively updated through the training process. By
means of extensive experiments, we show that our method can not only bring
remarkable improvement to baselines, but also overall be more stable, and
achieves state-of-the-art performance in semi-supervised text classification.

本文介绍了一种基于预训练语言模型的半监督学习方法，借助其内在的主题匹配能力构建一个更强大的半监督学习方法，该方法不仅能极大地改善基线结果，而且总体更加稳定，在半监督文本分类方面实现了最先进的性能。